00763nas a2200217 4500008004600000245007100046210006900117100002000186700001900206700002000225700001900245700001900264700002000283700002500303700002400328700002100352700002100373700001900394700002200413856011000435 Submitted eng d 00aBridging RDF and Property Graphs: Linking KnowWhereGraph and SPOKE0 aBridging RDF and Property Graphs Linking KnowWhereGraph and SPOK1 aStephen, Shirly1 aShimizu, Cogan1 aHitzler, Pascal1 aSoman, Karthik1 aRose, Peter, W1 aMorris, John, H1 aBaranzini, Sergio, E1 aJanowicz, Krzysztof1 aChristou, Antrea1 aDalal, Abhilekha1 aCurrier, Kitty1 aSchildhauer, Mark uhttps://daselab.cs.ksu.edu/publications/bridging-rdf-and-property-graphs-linking-knowwheregraph-and-spoke01918nas a2200133 4500008004600000245007900046210006900125520138500194100002101579700002701600700001801627700002001645856011901665 Submitted eng d 00aExplaining Deep Learning Hidden Neuron Activations using Concept Induction0 aExplaining Deep Learning Hidden Neuron Activations using Concept3 a
One of the current key challenges in Explainable AI is in correctly interpreting activations of hidden neurons. It seems evident that accurate interpretations thereof would provide insights into the question what a deep learning system has internally detected as relevant on the input, thus lifting some of the black box character of deep learning systems.
The state of the art on this front indicates that hidden node activations appear to be interpretable in a way that makes sense to humans, at least in some cases. Yet, systematic automated methods that would be able to first hypothesize an interpretation of hidden neuron activations, and then verify it, are mostly missing.
In this paper, we provide such a method and demonstrate that it provides meaningful interpretations. It is based on using large-scale background knowledge -- a class hierarchy of approx. 2 million classes curated from the Wikipedia Concept Hierarchy -- together with a symbolic reasoning approach called concept induction based on description logics that was originally developed for applications in the Semantic Web field.
Our results show that we can automatically attach meaningful labels from the background knowledge to individual neurons in the dense layer of a Convolutional Neural Network through a hypothesis and verification process.
1 aDalal, Abhilekha1 aSarker, Md Kamruzzaman1 aBarua, Adrita1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/explaining-deep-learning-hidden-neuron-activations-using-concept-induction00840nas a2200253 4500008004600000245006100046210005900107100001900166700002000185700002100205700001900226700003400245700002500279700002100304700001800325700002200343700002500365700001900390700002400409700002000433700002200453700002000475856009100495 Submitted eng d 00aKnowWhereGraph-Lite: A Perspective of the KnowWhereGraph0 aKnowWhereGraphLite A Perspective of the KnowWhereGraph1 aShimizu, Cogan1 aStephen, Shirly1 aChristou, Antrea1 aCurrier, Kitty1 aMahdavinejad, Mohammad, Saeid1 aNorouzi, Sanaz, Saki1 aDalal, Abhilekha1 aBarua, Adrita1 aFisher, Colby, K.1 aD’Onofrio, Anthony1 aThelen, Thomas1 aJanowicz, Krzysztof1 aRehberger, Dean1 aSchildhauer, Mark1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/knowwheregraph-lite-perspective-knowwheregraph00399nas a2200097 4500008004500000245006200045210006100107100001900168700002000187856009400207 In Press eng d 00aOntology-based Data Organization for the Enslaved Project0 aOntologybased Data Organization for the Enslaved Project1 aShimizu, Cogan1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/ontology-based-data-organization-enslaved-project00418nas a2200109 4500008004100000245005100041210005100092100002500143700003400168700002000202856008600222 2023 eng d00aConversational Ontology Alignment with ChatGPT0 aConversational Ontology Alignment with ChatGPT1 aNorouzi, Sanaz, Saki1 aMahdavinejad, Mohammad, Saeid1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/conversational-ontology-alignment-chatgpt00817nas a2200289 4500008004100000245003200041210002800073100001900101700002000120700001900140700002000159700001300179700002400192700002200216700003400238700002100272700001800293700001400311700001800325700001800343700001900361700002500380700001600405700001600421700002200437856006800459 2023 eng d00aThe KnowWhereGraph Ontology0 aKnowWhereGraph Ontology1 aShimizu, Cogan1 aStephen, Shirly1 aCurrier, Kitty1 aHitzler, Pascal1 aZhu, Rui1 aJanowicz, Krzysztof1 aSchildhauer, Mark1 aMahdavinejad, Mohammad, Saeid1 aDalal, Abhilekha1 aBarua, Adrita1 aCai, Ling1 aMai, Gengchen1 aWang, Zhangyu1 aTian, Yuanyuan1 aNorouzi, Sanaz, Saki1 aLiu, Zilong1 aShi, Meilin1 aFisher, Colby, K. uhttps://daselab.cs.ksu.edu/publications/knowwheregraph-ontology00914nas a2200313 4500008004100000245004400041210003900085100001900124700002000143700001300163700001900176700002200195700002000217700002000237700002400257700002200281700003400303700002100337700001800358700002100376700002500397700001600422700001600438700001400454700001800468700001800486700001900504856007700523 2023 eng d00aThe KnowWhereGraph Ontology: A Showcase0 aKnowWhereGraph Ontology A Showcase1 aShimizu, Cogan1 aStephen, Shirly1 aZhu, Rui1 aCurrier, Kitty1 aSchildhauer, Mark1 aRehberger, Dean1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aFisher, Colby, K.1 aMahdavinejad, Mohammad, Saeid1 aChristou, Antrea1 aBarua, Adrita1 aDalal, Abhilekha1 aNorouzi, Sanaz, Saki1 aLiu, Zilong1 aShi, Meilin1 aCai, Ling1 aMai, Gengchen1 aWang, Zhangyu1 aTian, Yuanyuan uhttps://daselab.cs.ksu.edu/publications/knowwheregraph-ontology-showcase01220nas a2200121 4500008004100000245010000041210006900141520069600210100002000906700001900926700002000945856013300965 2023 eng d00aMMODS-O: A Modular Ontology for the Metadata Object Description Schema (MODS) – Documentation0 aMMODSO A Modular Ontology for the Metadata Object Description Sc3 aWe are presenting the documentation for MMODS-O, an ontology derived from the Metadata Object Description Schema (MODS, version 3.8), which is an XML Schema by The Library of Congress. The XML Schema concerns metadata pertaining to bibliographic elements, however it is also used for other purposes, for instance LCACommons which is an interagency community that focues on Life Cycle Analysis, National Agricultural Library -- require the metadata to be in MODS format. Our motivation for developing this ontology -- including how it relates to previous attempts -- will be described elsewhere. This documentation is intended for readers who are familiar with MODS XML schema.
1 aRayan, Rushrukh1 aShimizu, Cogan1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/mmods-o-modular-ontology-metadata-object-description-schema-mods-%E2%80%93-documentation01231nas a2200133 4500008004100000245007100041210006500112520072800177100002000905700001900925700002200944700002000966856011100986 2023 eng d00aA Modular Ontology for MODS – Metadata Object Description Schema0 aModular Ontology for MODS Metadata Object Description Schema3 aThe Metadata Object Description Schema (MODS) was developed to describe bibliographic concepts and metadata and is maintained by the Library of Congress. Its authoritative version is given as an XML schema based on an XML mindset which means that it has significant limitations for use in a knowledge graphs context. We have therefore developed the Modular MODS Ontology (MMODS-O) which incorporates all elements and attributes of the MODS XML schema. In designing the ontology, we adopt the recent Modular Ontology Design Methodology (MOMo) with the intention to strike a balance between modularity and quality ontology design on the one hand, and conservative backward compatibility with MODS on the other.
1 aRayan, Rushrukh1 aShimizu, Cogan1 aSieverding, Heidi1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/modular-ontology-mods-%E2%80%93-metadata-object-description-schema01225nas a2200121 4500008004100000245005600041210005200097520080600149100002000955700001900975700002000994856008901014 2023 eng d00aAn Ontology Design Pattern for Role-Dependent Names0 aOntology Design Pattern for RoleDependent Names3 aWe present an ontology design pattern for modeling Names as part of Roles, to capture scenarios where an Agent performs different Roles using different Names associated with the different Roles. Examples of an Agent performing a Role using different Names are rather ubiq- uitous, e.g., authors who write under different pseudonyms, or different legal names for citizens of more than one country. The proposed pattern is a modified merger of a standard Agent Role and a standard Name pattern stub.
{Nominal schemas have been proposed as an extension to Description Logics (DL), the knowledge representation paradigm underlying the Web Ontology Language (OWL). They provide for a very tight integration of DL and rules. Nominal schemas can be understood as syntactic sugar on top of OWL. However, this naive perspective leads to inefficient reasoning procedures. In order to develop an efficient reasoning procedure for the language \\$\\{\\mathcal \\{E\\}\\mathcal \\{L\\}\\mathcal \\{V\\}^\\{++\\}\\}\\$, which results from extending the OWL profile language OWL EL with nominal schemas, we propose a transformation from \\$\\{\\mathcal \\{E\\}\\mathcal \\{L\\}\\mathcal \\{V\\}^\\{++\\}\\}\\$ ontologies into Datalog-like rule programs that can be used for satisfiability checking and assertion retrieval. The use of this transformation enables the use of powerful Datalog engines to solve reasoning tasks over \\$\\{\\mathcal \\{E\\}\\mathcal \\{L\\}\\mathcal \\{V\\}^\\{++\\}\\}\\$ ontologies. We implement and then evaluate our approach on several real-world, data-intensive ontologies, and find that it can outperform state-of-the-art reasoners such as Konclude and ELK. As a lesser side result we also provide a self-contained description of a rule-based algorithm for \\$\\{\\mathcal \\{E\\}\\mathcal \\{L\\}^\\{++\\}\\}\\$, which does not require a normal form transformation.}
1 aCarral, David1 aZalewski, Joseph1 aHitzler, Pascal uhttps://doi.org/10.1093/logcom/exac03201245nas a2200373 4500008004100000245016800041210006900209100002400278700002000302700001500322700002000337700002200357700001300379700001900392700002200411700001400433700001800447700002100465700001300486700002000499700002000519700001700539700002200556700002200578700001600600700001700616700001600633700001900649700001600668700001600684700002500700700001600725856013000741 2022 eng d00aKnow, Know Where, KnowWhereGraph: A Densely Connected, Cross-Domain Knowledge Graph and Geo-Enrichment Service Stack for Applications in Environmental Intelligence0 aKnow Know Where KnowWhereGraph A Densely Connected CrossDomain K1 aJanowicz, Krzysztof1 aHitzler, Pascal1 aLi, Wenwen1 aRehberger, Dean1 aSchildhauer, Mark1 aZhu, Rui1 aShimizu, Cogan1 aFisher, Colby, K.1 aCai, Ling1 aMai, Gengchen1 aZalewski, Joseph1 aZhou, Lu1 aStephen, Shirly1 aGonzalez, Seila1 aMecum, Bryce1 aCarr, Anna, Lopez1 aSchroeder, Andrew1 aSmith, Dave1 aWright, Dawn1 aWang, Sizhe1 aTian, Yuanyuan1 aLiu, Zilong1 aShi, Meilin1 aD’Onofrio, Anthony1 aGu, Zhining uhttps://daselab.cs.ksu.edu/publications/know-know-where-knowwheregraph-densely-connected-cross-domain-knowledge-graph-and-geo00591nas a2200169 4500008004100000245006600041210006500107260001900172100001600191700001600207700002400223700001800247700001300265700002400278700002000302856009900322 2022 eng d00aLD Connect: A Linked Data Portal for IOS Press Scientometrics0 aLD Connect A Linked Data Portal for IOS Press Scientometrics bSpringerc20221 aLiu, Zilong1 aShi, Meilin1 aJanowicz, Krzysztof1 aMai, Gengchen1 aZhu, Rui1 aDelbeque, Stephanie1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/ld-connect-linked-data-portal-ios-press-scientometrics00872nas a2200253 4500008004100000245007200041210006900113260002500182100002200207700003000229700002100259700001900280700002000299700002000319700002500339700002000364700001700384700003100401700002000432700001700452700001800469700002200487856010900509 2022 eng d00aNeural-Symbolic Learning and Reasoning: A Survey and Interpretation0 aNeuralSymbolic Learning and Reasoning A Survey and Interpretatio aAmsterdambIOS Press1 aBesold, Tarek, R.1 aGarcez, Artur, S. d'Avila1 aBader, Sebastian1 aBowman, Howard1 aDomingos, Pedro1 aHitzler, Pascal1 aKühnberger, Kai-Uwe1 aLamb, Luís, C.1 aLowd, Daniel1 aLima, Priscila, Machado Vi1 ade Penning, Leo1 aPinkas, Gadi1 aPoon, Hoifung1 aZaverucha, Gerson uhttps://daselab.cs.ksu.edu/publications/neural-symbolic-learning-and-reasoning-survey-and-interpretation00493nas a2200157 4500008004100000245005700041210005600098260001200154490000600166100002000172700002000192700002200212700002700234700001300261856006100274 2022 eng d00aNeuro-symbolic approaches in artificial intelligence0 aNeurosymbolic approaches in artificial intelligence c06/20220 v91 aHitzler, Pascal1 aEberhart, Aaron1 aEbrahimi, Monireh1 aSarker, Md Kamruzzaman1 aZhou, Lu uhttps://academic.oup.com/nsr/article/9/6/nwac035/654246000497nas a2200145 4500008004100000245005900041210005700100260000900157490000700166100002700173700001300200700002000213700002000233856009800253 2022 eng d00aNeuro-Symbolic Artificial Intelligence: Current Trends0 aNeuroSymbolic Artificial Intelligence Current Trends c20210 v341 aSarker, Md Kamruzzaman1 aZhou, Lu1 aEberhart, Aaron1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/neuro-symbolic-artificial-intelligence-current-trends00455nam a2200109 4500008004100000245006600041210006300107260002500170100002000195700002700215856010300242 2022 eng d00aNeuro-Symbolic Artificial Intelligence - The State of the Art0 aNeuroSymbolic Artificial Intelligence The State of the Art aAmsterdambIOS Press1 aHitzler, Pascal1 aSarker, Md Kamruzzaman uhttps://www.iospress.com/catalog/books/neuro-symbolic-artificial-intelligence-the-state-of-the-art00479nas a2200133 4500008004100000245005900041210005900100490000700159100002000166700002400186700001700210700001900227856009900246 2021 eng d00aAdvancing Agriculture through Semantic Data Management0 aAdvancing Agriculture through Semantic Data Management0 v121 aHitzler, Pascal1 aJanowicz, Krzysztof1 aSharda, Ajay1 aShimizu, Cogan uhttps://daselab.cs.ksu.edu/publications/advancing-agriculture-through-semantic-data-management00472nas a2200145 4500008004100000245004400041210004400085100001800129700001900147700001300166700002000179700003000199700002000229856007700249 2021 eng d00aAligning Patterns to the Wikibase Model0 aAligning Patterns to the Wikibase Model1 aEells, Andrew1 aShimizu, Cogan1 aZhou, Lu1 aHitzler, Pascal1 aEstrecha, Seila, Gonzalez1 aRehberger, Dean uhttps://daselab.cs.ksu.edu/publications/aligning-patterns-wikibase-model00457nas a2200097 4500008004100000245008700041210006900128100001900197700002000216856012300236 2021 eng d00aAutomatically Generating Human Readable Documentation for Ontology Design Patterns0 aAutomatically Generating Human Readable Documentation for Ontolo1 aShimizu, Cogan1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/automatically-generating-human-readable-documentation-ontology-design-patterns01563nas a2200121 4500008004100000245008100041210006900122520107200191100002101263700001901284700002001303856011801323 2021 eng d00aBridging Upper Ontology and Modular Ontology Modeling: A Tool and Evaluation0 aBridging Upper Ontology and Modular Ontology Modeling A Tool and3 aOntologies are increasingly used as schema for knowledge graphs in many application areas. As such, there are a variety of different approaches for their development. In this paper, we describe and evaluate UAO (for Upper Ontology Alignment Tool), which is an extension to CoModIDE, a graphical prote'ge' plugin for modular ontology modeling. UAO enables ontology engineers to combine modular ontology modeling with a more traditional ontology modeling approach based on upper ontologies. We posit -- and our evaluation supports this claim -- that the tool does indeed makes it easier to combine both approaches. Thus, UAO enables a best-of-both-worlds approach. The evaluation consists of a user study, and the results show that performing typical manual alignment modeling tasks is relatively easier with UAO than doing it with porte'ge' alone, in terms of the time required to complete the task and improving the correctness of the output. Additionally, our test subjects provided significantly higher ratings on the System Utilization Scale for UOA.
1 aDalal, Abhilekha1 aShimizu, Cogan1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/bridging-upper-ontology-and-modular-ontology-modeling-tool-and-evaluation00451nas a2200109 4500008004100000245007300041210006600114100002200180700002000202700002000222856009900242 2021 eng d00aOn the Capabilities of Pointer Networks for Deep Deductive Reasoning0 aCapabilities of Pointer Networks for Deep Deductive Reasoning1 aEbrahimi, Monireh1 aEberhart, Aaron1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/capabilities-pointer-networks-deep-deductive-reasoning01726nas a2200193 4500008004100000245005100041210005100092520112900143100001301272700002901285700001301314700001901327700001401346700001801360700002401378700002001402700002201422856008801444 2021 eng d00aEnvironmental Observations in Knowledge Graphs0 aEnvironmental Observations in Knowledge Graphs3 aThe notion of Linked Open Science rests on the assumption that Linked Data principles contribute to science and scientific data management in several distinct ways (e.g., by adding rich semantics to improve retrieval and reuse of data). This begs the question of the right level of granularity for such semantic enrichment. On the one extreme of the spectrum, one may provide semantic annotations on the level of entire datasets to improve retrieval while leaving the actual data untouched. On the other end, one may semantically describe every single datum, such as a particular observation leading to data that supports reasoning, automated conflation, and so on, while, at the same time, dramatically increasing the size of data, including redundancy. This paper reports on our experience in modeling heterogeneous environmental data using a semantically-enabled observation framework, namely the SOSA ontology and its extensions to handle observation collections. We discuss different means of using these observation collections and compare their pros and cons in terms of data size and ease of querying.
1 aZhu, Rui1 aAmbrose, Shirly, Stephen1 aZhou, Lu1 aShimizu, Cogan1 aCai, Ling1 aMai, Gengchen1 aJanowicz, Krzysztof1 aHitzler, Pascal1 aSchildhauer, Mark uhttps://daselab.cs.ksu.edu/publications/environmental-observations-knowledge-graphs01804nas a2200145 4500008004100000245004700041210004700088520133500135100002001470700001901490700002301509700002701532700002001559856007901579 2021 eng d00aExpressibility of OWL Axioms with Patterns0 aExpressibility of OWL Axioms with Patterns3 aThe high expressivity of the Web Ontology Language (OWL) makes it possible to describe complex relationships between classes, roles, and individuals in an ontology. However, this high expressivity can be an obstacle to correct usage and wide adoption. Past attempts to ameliorate this have included the development of specific, presumably human-friendly syntaxes, such as the Manchester syntax or graphical interfaces for OWL axioms, albeit with limited success. If modelers want to develop suitable OWL axioms it is important to make this as easy as possible. In this paper, we adopt an idea from the Protégé plug-in, OWLAx, which provides a simple, clickable interface to automatically input axioms of a limited number of types by following simple axiom patterns. In particular, each of these axiom patterns contains at most three classes or roles. We hypothesize that most of the axioms in existing ontologies could be expressed semantically in terms of simple patterns like these, which would mean that more complex patterns can be used very sparingly. Our findings, based on an analysis of 518 ontologies from six public ontology repositories, confirm this hypothesis: Over 90% of class axioms in the average ontology are indeed expressible with our simple patterns. We provide a detailed analysis of our findings.
1 aEberhart, Aaron1 aShimizu, Cogan1 aChowdhury, Sulogna1 aSarker, Md Kamruzzaman1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/expressibility-owl-axioms-patterns00630nas a2200145 4500008004100000245005200041210005000093490000700143520017500150100002100325700001300346700001900359700002000378856008600398 2021 eng d00aInK Browser - The Interactive Knowledge Browser0 aInK Browser The Interactive Knowledge Browser0 v203 aWe present an improved implementation of the Interactive Knowledge Browser (InK Browser), a tool for exploring knowledge graphs visually, using a schema diagram.
1 aZalewski, Joseph1 aZhou, Lu1 aShimizu, Cogan1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/ink-browser-interactive-knowledge-browser00337nas a2200109 4500008004100000245003000041210003000071100001900101700001700120700002000137856007000157 2021 eng d00aModular Ontology Modeling0 aModular Ontology Modeling1 aShimizu, Cogan1 aHammar, Karl1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/modular-ontology-modeling00650nas a2200181 4500008004100000245007600041210006900117260000900186100002200195700002700217700002200244700001400266700002000280700001700300700001900317700002000336856011200356 2021 eng d00aNeuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment0 aNeuroSymbolic Deductive Reasoning for CrossKnowledge Graph Entai bAAAI1 aEbrahimi, Monireh1 aSarker, Md Kamruzzaman1 aBianchi, Federico1 aXie, Ning1 aEberhart, Aaron1 aDoran, Derek1 aKim, HyeongSik1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/neuro-symbolic-deductive-reasoning-cross-knowledge-graph-entailment00502nas a2200157 4500008004100000245005200041210005100093260000900144490001000153100002000163700002400183700001900207700001300226700001800239856008700257 2021 eng d00aOpen Science data and the Semantic Web journal0 aOpen Science data and the Semantic Web journal c20210 v12(3)1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aShimizu, Cogan1 aZhou, Lu1 aEells, Andrew uhttps://daselab.cs.ksu.edu/publications/open-science-data-and-semantic-web-journal00524nas a2200157 4500008004100000245005800041210005600099260000800155100001900163700001300182700001800195700002200213700002400235700002000259856008700279 2021 eng d00aA Pattern for Features on a Hierarchical Spatial Grid0 aPattern for Features on a Hierarchical Spatial Grid bACM1 aShimizu, Cogan1 aZhu, Rui1 aMai, Gengchen1 aSchildhauer, Mark1 aJanowicz, Krzysztof1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/pattern-features-hierarchical-spatial-grid00511nas a2200145 4500008004100000245005900041210005700100100001900157700001300176700001700189700002200206700002400228700002000252856009300272 2021 eng d00aA Pattern for Modeling Causal Relations Between Events0 aPattern for Modeling Causal Relations Between Events1 aShimizu, Cogan1 aZhu, Rui1 aMai, Genchen1 aSchildhauer, Mark1 aJanowicz, Krzysztof1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/pattern-modeling-causal-relations-between-events00850nas a2200169 4500008004100000245003700041210003700078260001400115300001000129520037000139100002000509700001800529700002000547700001700567700002300584856007300607 2021 eng d00aSeed Patterns for Modeling Trees0 aSeed Patterns for Modeling Trees bIOS Press a48-673 aTrees – i.e., the type of data structure known under this name – are central to many aspects of knowledge organization. We investigate some central design choices concerning the ontological modeling of such trees. In particular, we consider the limits of what is expressible in the Web Ontology Language and provide a reusable ontology design pattern for trees.1 aEberhart, Aaron1 aCarral, David1 aHitzler, Pascal1 aLapp, Hilmar1 aRudolph, Sebastian uhttps://daselab.cs.ksu.edu/publications/seed-patterns-modeling-trees01537nas a2200157 4500008004100000245009300041210006900134520093500203653002301138653002101161653000901182100002101191700002001212700002401232856012301256 2021 eng d00aSemantic Compression with Region Calculi in Nested Hierarchical Grids (Technical Report)0 aSemantic Compression with Region Calculi in Nested Hierarchical 3 aWe propose the combining of region connection calculi with nested hierarchical grids for representing spatial region data in the context of knowledge graphs, thereby avoiding reliance on vector representations. We present a resulting region calculus, and provide qualitative and formal evidence that this representation can be favorable with large data volumes in the context of knowledge graphs; in particular we study means of efficiently choosing which triples to store to minimize space requirements when data is represented this way, and we provide an algorithm for finding the smallest possible set of triples for this purpose including an asymptotic measure of the size of this set for a special case. We prove that a known constraint calculus is adequate for the reconstruction of all triples describing a region from such a pruned representation, but problematic for reasoning with hierarchical grids in general.
10aHierarchical Grids10aKnowledge Graphs10aRCC51 aZalewski, Joseph1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttps://daselab.cs.ksu.edu/publications/semantic-compression-region-calculi-nested-hierarchical-grids-technical-report01566nas a2200193 4500008004100000020001800041245007400059210006900133260005900202300001400261520093500275653002301210653002101233653000901254100002101263700002001284700002401304856004401328 2021 eng d a978145038664700aSemantic Compression with Region Calculi in Nested Hierarchical Grids0 aSemantic Compression with Region Calculi in Nested Hierarchical aNew York, NY, USAbAssociation for Computing Machinery a305–3083 aWe propose the combining of region connection calculi with nested hierarchical grids for representing spatial region data in the context of knowledge graphs, thereby avoiding reliance on vector representations. We present a resulting region calculus, and provide qualitative and formal evidence that this representation can be favorable with large data volumes in the context of knowledge graphs; in particular we study means of efficiently choosing which triples to store to minimize space requirements when data is represented this way, and we provide an algorithm for finding the smallest possible set of triples for this purpose including an asymptotic measure of the size of this set for a special case. We prove that a known constraint calculus is adequate for the reconstruction of all triples describing a region from such a pruned representation, but problematic for reasoning with hierarchical grids in general.
10aHierarchical Grids10aKnowledge Graphs10aRCC51 aZalewski, Joseph1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttps://doi.org/10.1145/3474717.348396500689nas a2200193 4500008004100000245009300041210006900134260000800203100001300211700001900224700002000243700001300263700001400276700001800290700002400308700002200332700002000354856012100374 2021 eng d00aSOSA-SHACL: Shapes Constraint for the Sensor, Observation, Sample, and Actuator Ontology0 aSOSASHACL Shapes Constraint for the Sensor Observation Sample an bACM1 aZhu, Rui1 aShimizu, Cogan1 aStephen, Shirly1 aZhou, Lu1 aCai, Ling1 aMai, Gengchen1 aJanowicz, Krzysztof1 aSchildhauer, Mark1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/sosa-shacl-shapes-constraint-sensor-observation-sample-and-actuator-ontology01539nas a2200205 4500008004100000245004500041210004500086250000700131520093100138100001801069700002001087700002001107700002501127700001901152700002001171700002001191700002001211700001701231856008501248 2021 eng d00aToward Undifferentiated Cognitive Models0 aToward Undifferentiated Cognitive Models a193 aAutonomous systems are a new frontier for pushing sociotechnical advancement. Such systems will eventually become pervasive, involved in everything from manufacturing, healthcare, defense, and even research itself. However, proliferation is stifled by the high development costs and the resulting inflexibility of the produced systems. The current time needed to create and integrate state of the art autonomous systems that operate as team members in complex situations is a 3-15 year development period, often requiring humans to adapt to limitations in the resulting systems. A new research thrust in interactive task learning (ITL) has begun, calling for natural human-autonomy interaction to facilitate system flexibility and minimize users’ complexity in providing autonomous systems with new tasks. We discuss the development of an undifferentiated agent with a modular framework as a method of approaching that goal.1 aKupitz, Colin1 aEberhart, Aaron1 aSchmidt, Daniel1 aStevens, Christopher1 aShimizu, Cogan1 aHitzler, Pascal1 aSalvucci, Dario1 aMaruyama, Benji1 aMyers, Chris uhttps://daselab.cs.ksu.edu/publications/toward-undifferentiated-cognitive-models00484nas a2200133 4500008004100000245005800041210005800099260001400157100001900171700002200190700002000212700002400232856009400256 2021 eng d00aTowards a Modular Ontology for Space Weather Research0 aTowards a Modular Ontology for Space Weather Research bIOS Press1 aShimizu, Cogan1 aMcGranaghan, Ryan1 aEberhart, Aaron1 aKellerman, Adam, C. uhttps://daselab.cs.ksu.edu/publications/towards-modular-ontology-space-weather-research-000490nas a2200121 4500008004100000245007000041210006800111100002200179700002000201700002200221700002000243856010500263 2021 eng d00aTowards Bridging the Neuro-Symbolic Gap: Deep Deductive Reasoners0 aTowards Bridging the NeuroSymbolic Gap Deep Deductive Reasoners1 aEbrahimi, Monireh1 aEberhart, Aaron1 aBianchi, Federico1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/towards-bridging-neuro-symbolic-gap-deep-deductive-reasoners00388nas a2200109 4500008004100000245005100041210005000092260005200142490000800194100002200202856005400224 2021 eng d00aTowards generalizable neuro-symbolic reasoners0 aTowards generalizable neurosymbolic reasoners aManhattan, KSbKansas State Universityc08/20210 vPhD1 aEbrahimi, Monireh uhttps://krex.k-state.edu/dspace/handle/2097/4162102312nas a2200133 4500008004100000245007700041210006900118260004800187300000800235490001000243520179300253100001902046856011302065 2020 eng d00aAdvances in modular ontology engineering: methodology and infrastructure0 aAdvances in modular ontology engineering methodology and infrast aManhattanbKansas State Universityc08/2020 a1750 vPh.D.3 aModular ontology engineering is a methodology for producing highly reusable knowledge graph schema. Over the course of this dissertation, we outline a number of contributions that have improved the process to what we see today. These contributions fall within four categories: conveying meaning through schema diagrams, the composition of a modular ontology, the modular ontology engineering methodology, and modular graphical modeling.
First, we created an improved method and tool for generating schema diagrams similar to those manually generated by humans and show that most of OWL, as it is used in real world ontologies, are expressible in this format.
Next, we examined and improved the ontology design pattern development process. This was accomplished through the development of both patterns and modules, extensions to the ontology design pattern representation language, and a tool that significantly improves the usability of these annotations. This work culminated in MODL: a modular ontology design library, which is a distributable set of curated, well-documented ODPs, both novel and drawn from the ontology design pattern portal.
These advances were combined, and building upon the state of the art, to create the Comprehensive Modular Ontology Design IDE (CoModIDE), which is a plugin for the industry-standard ontology editor, Protege.
Finally, as a culmination of the tool and the methodology, we evaluated CoModIDE, where it was shown to significantly improve outcomes for experienced and new ontology developers when developing modular ontologies.
Altogether, these research topics, resulted in a methodology, that when executed, produced actually reusable, extendable, and adaptable ontologies.
1 aShimizu, Cogan uhttps://daselab.cs.ksu.edu/publications/advances-modular-ontology-engineering-methodology-and-infrastructure00333nas a2200109 4500008004100000245003000041210003000071260002200101100001300123700002000136856006700156 2020 eng d00aAROA Results of OAEI 20200 aAROA Results of OAEI 2020 bSpringerc12/20201 aZhou, Lu1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/aroa-results-oaei-202001414nas a2200241 4500008004100000245006500041210006400106260007400170490000900244520070200253653001800955653002200973653000800995653000901003653000901012653001401021100002001035700002201055700001301077700001901090700002001109856004301129 2020 eng d00aCompletion Reasoning Emulation for the Description Logic EL+0 aCompletion Reasoning Emulation for the Description Logic EL aStanford University, Palo Alto, California, USAbCEUR-WS.orgc03/20200 v26003 aWe present a new approach to integrating deep learning with knowledge-based systems that we believe shows promise. Our approach seeks to emulate reasoning structure, which can be inspected part-way through, rather than simply learning reasoner answers, which is typical in many of the black-box systems currently in use. We demonstrate that this idea is feasible by training a long short-term memory (LSTM) artificial neural network to learn EL+ reasoning patterns with two different data sets. We also show that this trained system is resistant to noise by corrupting a percentage of the test data and comparing the reasoner's and LSTM's predictions on corrupt data with correct answers.
10aDeep Learning10aDescription Logic10aEL+10aLSTM10aNeSy10aReasoning1 aEberhart, Aaron1 aEbrahimi, Monireh1 aZhou, Lu1 aShimizu, Cogan1 aHitzler, Pascal uhttp://ceur-ws.org/Vol-2600/paper5.pdf00599nas a2200145 4500008003900000245008900039210006900128100001600197700002100213700002100234700002000255700002200275700002700297856012900324 2020 d00aCounterfactual reasoning over large-scale human performance optimization experiments0 aCounterfactual reasoning over largescale human performance optim1 aJuvina, Ion1 aAue, William, R.1 aMinnery, Brandon1 aHitzler, Pascal1 aNadella, Srikanth1 aSarker, Md Kamruzzaman uhttps://daselab.cs.ksu.edu/publications/counterfactual-reasoning-over-large-scale-human-performance-optimization-experiments00630nas a2200169 4500008004100000245009200041210006900133260000800202300001400210100001800224700001600242700002000258700002000278700001700298700001800315856012700333 2020 eng d00aCSSA'20: Workshop on Combining Symbolic and Sub-Symbolic Methods and their Applications0 aCSSA20 Workshop on Combining Symbolic and SubSymbolic Methods an bACM a3523-35241 aAlam, Mehwish1 aGroth, Paul1 aHitzler, Pascal1 aPaulheim, Heiko1 aSack, Harald1 aTresp, Volker uhttps://daselab.cs.ksu.edu/publications/cssa20-workshop-combining-symbolic-and-sub-symbolic-methods-and-their-applications00520nas a2200121 4500008004100000245006900041210006700110260005500177100001900232700002200251700002400273856010100297 2020 eng d00aData Integration with Knowledge Graphs: A Space Weather Use-case0 aData Integration with Knowledge Graphs A Space Weather Usecase aAmerican Geophysical Union (AGU) Fall Meeting 20201 aShimizu, Cogan1 aMcGranaghan, Ryan1 aKellerman, Adam, C. uhttps://daselab.cs.ksu.edu/publications/data-integration-knowledge-graphs-space-weather-use-case01281nas a2200157 4500008004100000245004400041210004200085520078800127100002000915700001900935700002500954700002000979700002700999700001901026856007801045 2020 eng d00aA Domain Ontology for Task Instructions0 aDomain Ontology for Task Instructions3 a Knowledge graphs and ontologies represent information in a variety of different applications. One use case, the Intelligence, Surveillance, & Reconnaissance: Mutli-Attribute Task Battery (ISR-MATB), comes from Cognitive Science, where researchers use interdisciplinary methods to understand the mind and cognition. The ISR-MATB is a set of tasks that a cognitive or human agent perform which test visual, auditory, and memory capabilities. An ontology can represent a cognitive agent’s background knowledge of the task it was instructed to perform and act as an interchange format between different Cognitive Agent tasks similar to ISR-MATB. We present several modular patterns for representing ISR-MATB task instructions, as well as a unified diagram that links them together.1 aEberhart, Aaron1 aShimizu, Cogan1 aStevens, Christopher1 aHitzler, Pascal1 aMyers, Christopher, W.1 aMaruyam, Benji uhttps://daselab.cs.ksu.edu/publications/domain-ontology-task-instructions00686nas a2200181 4500008004100000245009100041210006900132260001700201100001300218700001900231700002000250700002200270700003000292700002100322700001700343700002000360856012400380 2020 eng d00aThe Enslaved Dataset: A Real-world Complex Ontology Alignment Benchmark using Wikibase0 aEnslaved Dataset A Realworld Complex Ontology Alignment Benchmar bACMc10/20201 aZhou, Lu1 aShimizu, Cogan1 aHitzler, Pascal1 aSheill, Alicia, M1 aEstrecha, Seila, Gonzalez1 aFoley, Catherine1 aTarr, Duncan1 aRehberger, Dean uhttps://daselab.cs.ksu.edu/publications/enslaved-dataset-real-world-complex-ontology-alignment-benchmark-using-wikibase01713nas a2200313 4500008004100000245006300041210005800104260001200162490000700174520076200181653002100943653002300964653003100987653002101018653002901039100001901068700002001087700001601107700002001123700003001143700002101173700002301194700002201217700001701239700001901256700001601275700001701291856009101308 2020 eng d00aThe Enslaved Ontology: Peoples of the Historic Slave Trade0 aEnslaved Ontology Peoples of the Historic Slave Trade c08/20200 v633 aWe present the Enslaved Ontology (V1.0) which was developed for integrating data about the historic slave trade from diverse sources in a use case driven by historians. Ontology development followed modular ontology design principles as derived from ontology design pattern application best practices and the eXtreme Design Methodology. Ontology content focuses on data about historic persons and the event records from which this data can be taken. It also incorporates provenance modeling and some temporal and spatial aspects. The ontology is available as serialized in the Web Ontology Language OWL, and carries modularization annotations using the Ontology Pattern Language (OPLa). It is available under the Creative Commons CC BY 4.0 license.
10adata integration10adigital humanities10ahistory of the slave trade10amodular ontology10aOntology Design Patterns1 aShimizu, Cogan1 aHitzler, Pascal1 aHirt, Quinn1 aRehberger, Dean1 aEstrecha, Seila, Gonzalez1 aFoley, Catherine1 aSheill, Alicia, M.1 aHawthorne, Walter1 aMixter, Jeff1 aWatrall, Ethan1 aCarty, Ryan1 aTarr, Duncan uhttps://daselab.cs.ksu.edu/publications/enslaved-ontology-peoples-historic-slave-trade00558nas a2200133 4500008004100000245008300041210006900124100002500193700002100218700002800239700002000267700002000287856011700307 2020 eng d00aA Framework for Explainable Deep Neural Models Using External Knowledge Graphs0 aFramework for Explainable Deep Neural Models Using External Know1 aDaniels, Zachary, A.1 aFrank, Logan, D.1 aMenart, Christopher, J.1 aRaymer, Michael1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/framework-explainable-deep-neural-models-using-external-knowledge-graphs01070nas a2200121 4500008003900000245002900039210002700068490000900095520074100104100002000845700002000865856006300885 2020 d00aA Functional API for OWL0 aFunctional API for OWL0 v27213 aWe present (f OWL), a minimalistic, functional programming style ontology editor that is based directly on the OWL 2 Structural Specification. (f OWL) is written from scratch, entirely in Clojure, having no other dependencies. Ontologies in (f OWL) are implemented as standalone and homogeneous data structures, which means that the same exact functions written for single axioms or expressions often work identically on any part of an ontology, even the entire ontology itself. The lazy functional style of Clojure also allows for intuitive and simple ontology creation and modification with a minimal memory footprint. All of this is possible without ever needing to use a single class, except of course in the Ontologies one creates!1 aEberhart, Aaron1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/functional-api-owl00525nas a2200121 4500008004100000245009500041210006900136260002000205100002000225700001300245700002000258856012500278 2020 eng d00aGeoLink Cruises: A Non-Synthetic Benchmark for Co-Reference Resolution on Knowledge Graphs0 aGeoLink Cruises A NonSynthetic Benchmark for CoReference Resolut bACM DLc10/20201 aAmini, Reihaneh1 aZhou, Lu1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/geolink-cruises-non-synthetic-benchmark-co-reference-resolution-knowledge-graphs00493nas a2200121 4500008004100000245007600041210006900117100001300186700002300199700002100222700002000243856010800263 2020 eng d00aGeoLink Dataset: A Complex Alignment Benchmark from Real-world Ontology0 aGeoLink Dataset A Complex Alignment Benchmark from Realworld Ont1 aZhou, Lu1 aCheatham, Michelle1 aKrisnadhi, Adila1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/geolink-dataset-complex-alignment-benchmark-real-world-ontology00403nas a2200109 4500008004100000245005500041210005400096490000700150100002400157700002000181856009200201 2020 eng d00aGold-Level Open Access at the Semantic Web Journal0 aGoldLevel Open Access at the Semantic Web Journal0 v111 aJanowicz, Krzysztof1 aHitzler, Pascal uhttp://www.semantic-web-journal.net/content/gold-level-open-access-semantic-web-journal00568nam a2200133 4500008004100000245010300041210006900144260002500213490000700238100001800245700002000263700002000283856013100303 2020 eng d00aKnowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges0 aKnowledge Graphs for eXplainable Artificial Intelligence Foundat aAmsterdambIOS Press0 v471 aTiddi, Ilaria1 aLécué, Freddy1 aHitzler, Pascal uhttps://www.iospress.nl/book/knowledge-graphs-for-explainable-artificial-intelligence-foundations-applications-and-challenges/00697nas a2200241 4500008004100000245005300041210005300094260001300147300001200160490001000172100001900182700001700201700002000218700001900238700002100257700003200278700002000310700001600330700002400346700001700370700002000387856004800407 2020 eng d00aModular Graphical Ontology Engineering Evaluated0 aModular Graphical Ontology Engineering Evaluated bSpringer a20–350 v121231 aShimizu, Cogan1 aHammar, Karl1 aHitzler, Pascal1 aHarth, Andreas1 aKirrane, Sabrina1 aNgomo, Axel-Cyrille, Ngonga1 aPaulheim, Heiko1 aRula, Anisa1 aGentile, Anna, Lisa1 aHaase, Peter1 aCochez, Michael uhttps://doi.org/10.1007/978-3-030-49461-2\201071nas a2200157 4500008004100000024002200041245004200063210004100105260001400146490000700160520060700167100001900774700002000793700002100813856007900834 2020 eng d a10.3233/SSW20003200aModular Ontology Modeling: A Tutorial0 aModular Ontology Modeling A Tutorial bIOS Press0 v493 aWe provide an in-depth example of modular ontology engineering with ontology design patterns. The style and content of this chapter is adapted from previous work and tutorials on Modular Ontology Modeling. It o ers expanded steps and updated tool information. The tutorial is largely self-contained, but assumes that the reader is familiar with the Web Ontology Language OWL; however, we do briefly review some foundational concepts. By the end of the tutorial, we expect
the reader to have an understanding of the underlying motivation and methodology for producing a modular ontology.
We provide an extension to the Prote'ge'-based modular ontology engineering tool CoModIDE, in order to make it possible for ontology engineers to adhere to traditional ontology modeling processes based on upper or foundational ontologies. As a bridge between the more recently proposed modular ontology modeling approach and more classical ones based on foundational ontologies, it enables a best-of-both-worlds approach for ontology engineering.
1 aDalal, Abhilekha1 aShimizu, Cogan1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/modular-ontology-modeling-meets-upper-ontologies-upper-ontology-alignment-tool02942nas a2200133 4500008004100000245008900041210006900130260004800199300000700247490001200254520239600266100002102662856012502683 2020 eng d00aModular Ontology Modeling Meets Upper Ontologies: The Upper Ontology Alignment Tool0 aModular Ontology Modeling Meets Upper Ontologies The Upper Ontol aManhattanbKansas State Universityc11/2020 a350 vMasters3 aOntology modeling has become a primary approach to schema generation for data integration and knowledge graphs in many application areas. The quest for efficient approaches to model useful and re-useable ontologies has led to different ontology creation proposals over the years. The project focuses on two major approaches, modeling using a top-level ontology, and the other is modular ontology modeling.
The traditional approach is based on top-level ontology, and the strategy is to utilize ontology that is comprehensive enough to cover a broad spectrum of domains through their universal terminologies. In this way, all domain ontologies share a common top-level formal ontology in which their respective root nodes can be defined, and hence consistency is assured across the knowledge graph. Nevertheless, the most recent approach is quite different and is a refinement of the eXtreme Ontology Design methodology based on the ontology design patterns. Whole ontology is viewed as a collection of interconnected modules, and modules are developed around the classified fundamental notions according to experts' terminology or the use-case. Having developed modules in a fashion of divide and conquer, these modules are shareable and reusable among some other ontology if needed, and consequently, the ontology being FAIR is justified (findable, accessible, interoperable, and reusable).
Although, it has been argued that there are advantages to either paradigm, it is possible to have a combination of both approaches mentioned earlier, depending upon the use-case or the preferences of the ontology engineers. We provide an extension to the Protégé - based modular ontology engineering tool CoModIDE, in order to make it possible for ontology engineers to follow traditional, ad-hoc ontology modeling approach, alongside more modern paradigms such as modular ontology engineering. The project focuses on domain-level ontology developers or organizations dealing with ontology development, which may get help through the plugin in minimizing the tooling gap to unite paradigms and develop robust, flexible ontologies suitable to their needs. As a bridge between the more recently proposed modular ontology modeling approach and more classical ones based on foundational ontologies, it enables a best-of-both-worlds approach for ontology engineering.
1 aDalal, Abhilekha uhttps://daselab.cs.ksu.edu/publications/modular-ontology-modeling-meets-upper-ontologies-upper-ontology-alignment-tool-002855nas a2200277 4500008004100000245005400041210005400095520195300149653003302102653002302135653003302158653001502191653001502206100002802221700003402249700001902283700002002302700001602322700002802338700003202366700002102398700001702419700002302436700002002459856009802479 2020 eng d00aMultimodal mental health analysis in social media0 aMultimodal mental health analysis in social media3 ap.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.5px Helvetica}
Depression is a major public health concern in the U.S. and globally. While successful early
identification and treatment can lead to many positive health and behavioral outcomes,
depression, remains undiagnosed, untreated or undertreated due to several reasons,
including denial of the illness as well as cultural and social stigma. With the ubiquity of social
media platforms, millions of people are now sharing their online persona by expressing their
thoughts, moods, emotions, and even their daily struggles with mental health on social
media. Unlike traditional observational cohort studies conducted through questionnaires
and self-reported surveys, we explore the reliable detection of depressive symptoms from
tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social)
data to discern depressive behaviors using a wide variety of features including individuallevel
demographics. By developing a multimodal framework and employing statistical techniques
to fuse heterogeneous sets of features obtained through the processing of visual,
textual, and user interaction data, we significantly enhance the current state-of-the-art
approaches for identifying depressed individuals on Twitter (improving the average F1-
Score by 5 percent) as well as facilitate demographic inferences from social media. Besides
providing insights into the relationship between demographics and mental health, our
research assists in the design of a new breed of demographic-aware health interventions.
10aExplainable Machine Learning10aHypothesis Testing10aNational Language Processing10aPrediction10aRegression1 aYazdavar, Amir, Hossein1 aMahdavinejad, Mohammad, Saeid1 aBaja, Goonmeet1 aRomine, William1 aSheth, Amit1 aMonadjemi, Amir, Hassan1 aThirunarayan, Krishnaprasad1 aMeddar, John, M.1 aMyers, Annie1 aPathak, Jyotishman1 aHitzler, Pascal uhttps://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248&type=printable00473nas a2200133 4500008004100000245005300041210005200094490000700146100002000153700002200173700002200195700002700217856009500244 2020 eng d00aNeural-Symbolic Integration and the Semantic Web0 aNeuralSymbolic Integration and the Semantic Web0 v111 aHitzler, Pascal1 aBianchi, Federico1 aEbrahimi, Monireh1 aSarker, Md Kamruzzaman uhttp://www.semantic-web-journal.net/content/neural-symbolic-integration-and-semantic-web-000542nas a2200181 4500008004100000245003500041210003100076100001900107700002000126700002000146700001600166700002500182700002700207700002000234700001800254700002000272856006800292 2020 eng d00aAn Ontology of Instruction 1.00 aOntology of Instruction 101 aShimizu, Cogan1 aHitzler, Pascal1 aEberhart, Aaron1 aHirt, Quinn1 aStevens, Christopher1 aMyers, Christopher, W.1 aMaruyama, Benji1 aKupitz, Colin1 aSalvucci, Dario uhttps://daselab.cs.ksu.edu/publications/ontology-instruction-1001204nas a2200397 4500008004100000245006400041210006400105100002700169700002300196700002000219700001800239700002100257700001600278700001900294700002700313700002100340700001800361700002400379700001800403700002100421700001500442700001300457700002000470700002000490700002000510700002000530700001900550700002300569700002100592700002000613700002200633700001700655700002000672700001300692856010100705 2020 eng d00aResults of theOntology Alignment Evaluation Initiative 20200 aResults of theOntology Alignment Evaluation Initiative 20201 aPour, Mina, Abd Nikooi1 aAlgergawy, Alsayed1 aAmini, Reihaneh1 aFaria, Daniel1 aFundulaki, Irini1 aHarrow, Ian1 aHertling, Sven1 aJiménez-Ruiz, Ernesto1 aJonquet, Clement1 aKaram, Naouel1 aKhiat, Abderrahmane1 aLaadhar, Amir1 aLambrix, Patrick1 aLi, Huanyu1 aLi, Ying1 aHitzler, Pascal1 aPaulheim, Heiko1 aPesquita, Catia1 aSaveta, Tzanina1 aShvaiko, Pavel1 aSplendiani, Andrea1 aThieblin, Elodie1 aTrojahn, Cassia1 aVatascinova, Jana1 aYaman, Beyza1 aZamazal, Ondrej1 aZhou, Lu uhttps://daselab.cs.ksu.edu/publications/results-theontology-alignment-evaluation-initiative-202000470nas a2200097 4500008004100000245004000041210003700081520016400118100002000282856007000302 2020 eng d00a A Review Of The Semantic Web Field0 aReview Of The Semantic Web Field3 aWe review two decades of Semantic Web research and applications, discuss relationships to some other disciplines, and current challenges in the field.
1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/review-semantic-web-field00433nas a2200133 4500008004100000245007200041210006900113300001400182490000700196100001900203700002000222700001900242856003800261 2020 eng d00aTime is ripe to embrace the scientific approach in Applied Ontology0 aTime is ripe to embrace the scientific approach in Applied Ontol a245–2490 v151 aBorgo, Stefano1 aHitzler, Pascal1 aShimizu, Cogan uhttps://doi.org/10.3233/AO-20023700432nas a2200133 4500008004100000245005800041210005800099490001900157100001900176700002200195700002000217700002400237856003700261 2020 eng d00aTowards a Modular Ontology for Space Weather Research0 aTowards a Modular Ontology for Space Weather Research0 vabs/2009.122851 aShimizu, Cogan1 aMcGranaghan, Ryan1 aEberhart, Aaron1 aKellerman, Adam, C. uhttps://arxiv.org/abs/2009.1228500492nas a2200109 4500008004100000245008300041210006900124260003900193490001400232100001300246856012300259 2020 eng d00aTowards automated complex ontology alignment using rule-based machine learning0 aTowards automated complex ontology alignment using rulebased mac aManhattanbKansas State University0 vDoctorate1 aZhou, Lu uhttps://daselab.cs.ksu.edu/publications/towards-automated-complex-ontology-alignment-using-rule-based-machine-learning00546nas a2200169 4500008004100000245005100041210005100092490000700143100001300150700002100163700002300184700001800207700002000225700002000245700002000265856009100285 2020 eng d00aTowards Evaluating Complex Ontology Alignments0 aTowards Evaluating Complex Ontology Alignments0 v351 aZhou, Lu1 aThieblin, Elodie1 aCheatham, Michelle1 aFaria, Daniel1 aPesquita, Catia1 aTrojahn, Cassia1 aZamazal, Ondrej uhttps://daselab.cs.ksu.edu/publications/towards-evaluating-complex-ontology-alignments01683nas a2200205 4500008004100000245004900041210004900090260001200139520105600151100002701207700002101234700002001255700001301275700002201288700002101310700001601331700002401347700002101371856008501392 2020 eng d00aWikipedia Knowledge Graph for Explainable AI0 aWikipedia Knowledge Graph for Explainable AI c11/20203 aExplainable artificial intelligence (XAI) requires domain information to explain a system's decisions, for which structured forms of domain information like Knowledge Graphs (KGs) or ontologies are best suited. As such, readily available KGs are important to accelerate progress in XAI. To facilitate the advancement of XAI, we present the Wikipedia Knowledge Graph (WKG), based on information from English Wikipedia. Each Wikipedia article title, its corresponding category, and the category hierarchy are transformed into different entities in the knowledge graph. As the Wikipedia category hierarchy is not a tree, instead forming a graph, to make the finding process of the parent category easier, we break cycles in the category hierarchy. We evaluate whether the WKG is helpful to improve XAI compared with existing KGs, finding that WKG is better suited than the current state of the art. We also compare the cycle-free WKG with the Suggested Upper Merged Ontology (SUMO) and DBpedia schema KGs, finding minimal to no information loss.
1 aSarker, Md Kamruzzaman1 aSchwartz, Joshua1 aHitzler, Pascal1 aZhou, Lu1 aNadella, Srikanth1 aMinnery, Brandon1 aJuvina, Ion1 aRaymer, Michael, L.1 aAue, William, R. uhttps://daselab.cs.ksu.edu/publications/wikipedia-knowledge-graph-explainable-ai00518nas a2200121 4500008004100000245009100041210006900132100002300201700001900224700001800243700001300261856012200274 2019 eng d00aAlignment of Surface Water Ontologies: A comparison of manual and automated approaches0 aAlignment of Surface Water Ontologies A comparison of manual and1 aCheatham, Michelle1 aVaranka, Dalia1 aArauz, Fatima1 aZhou, Lu uhttps://daselab.cs.ksu.edu/publications/alignment-surface-water-ontologies-comparison-manual-and-automated-approaches00378nas a2200121 4500008004100000245003000041210003000071260003200101100001300133700002300146700002000169856006700189 2019 eng d00aAROA Results of 2019 OAEI0 aAROA Results of 2019 OAEI aAuckland, New ZealandbCEUR1 aZhou, Lu1 aCheatham, Michelle1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/aroa-results-2019-oaei00419nas a2200097 4500008004100000245007300041210006600114100002200180700002000202856009900222 2019 eng d00aOn the Capabilities of Logic Tensor Networks for Deductive Reasoning0 aCapabilities of Logic Tensor Networks for Deductive Reasoning1 aBianchi, Federico1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/capabilities-logic-tensor-networks-deductive-reasoning00375nas a2200109 4500008004100000245006300041210006000104100001900164700002000183700002400203856003800227 2019 eng d00aA closer look at the Semantic Web journal's review process0 acloser look at the Semantic Web journals review process1 aShimizu, Cogan1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttps://doi.org/10.3233/SW-18034200369nas a2200097 4500008004100000245005400041210005200095100001900147700001700166856008800183 2019 eng d00aCoModIDE - The Comprehensive Modular Ontology IDE0 aCoModIDE The Comprehensive Modular Ontology IDE1 aShimizu, Cogan1 aHammar, Karl uhttps://daselab.cs.ksu.edu/publications/comodide-comprehensive-modular-ontology-ide00506nas a2200133 4500008004100000245006600041210006500107260001300172100002200185700002200207700002000229700002200249856010100271 2019 eng d00aComplementing Logical Reasoning with Sub-symbolic Commonsense0 aComplementing Logical Reasoning with Subsymbolic Commonsense bSpringer1 aBianchi, Federico1 aPalmonari, Matteo1 aHitzler, Pascal1 aSerafini, Luciano uhttps://daselab.cs.ksu.edu/publications/complementing-logical-reasoning-sub-symbolic-commonsense00428nas a2200133 4500008004100000245005700041210005600098260000900154300001400163100002100177700001900198700003200217856004500249 2019 eng d00aConstrained State-Preserved Extreme Learning Machine0 aConstrained StatePreserved Extreme Learning Machine bIEEE a752–7591 aGoodman, Garrett1 aShimizu, Cogan1 aKtistakis, Iosif, Papadakis uhttps://doi.org/10.1109/ICTAI.2019.0010901687nas a2200133 4500008004100000245005500041210005500096260003200151490000700183520122500190100002701415700002001442856009101462 2019 eng d00aEfficient Concept Induction for Description Logics0 aEfficient Concept Induction for Description Logics aHonolulu, USbAAAIc01/20190 v333 aConcept Induction refers to the problem of creating complex Description Logic class descriptions (i.e., TBox axioms) from instance examples (i.e., ABox data). In this paper we look particularly at the case where both a set of positive and a set of negative instances are given, and complex class expressions are sought under which the positive but not the negative examples fall. Concept induction has found applications in ontology engineering, but existing algorithms have fundamental performance issues in some scenarios, mainly because a high number of invokations of an external Description Logic reasoner is usually required. In this paper we present a new algorithm for this problem which drastically reduces the number of reasoner invokations needed. While this comes at the expense of a more limited traversal of the search space, we show that our approach improves execution times by up to several orders of magnitude, while output correctness, measured in the amount of correct coverage of the input instances, remains reasonably high in many cases. Our approach thus should provide a strong alternative to existing systems, in particular in settings where other systems are prohibitively slow.
1 aSarker, Md Kamruzzaman1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/efficient-concept-induction-description-logics02096nas a2200193 4500008004100000020002200041245008900063210006900152260004400221300001400265520137900279100002201658700001901680700002201699700002301721700001701744700002501761856011601786 2019 eng d a978-3-030-05710-700aExploring the Impact of Training Data Bias on Automatic Generation of Video Captions0 aExploring the Impact of Training Data Bias on Automatic Generati aChambSpringer International Publishing a178–1903 aA major issue in machine learning is availability of training data. While this historically referred to the availability of a sufficient volume of training data, recently this has shifted to the availability of sufficient unbiased training data. In this paper we focus on the effect of training data bias on an emerging multimedia application, the automatic captioning of short video clips. We use subsets of the same training data to generate different models for video captioning using the same machine learning technique and we evaluate the performances of different training data subsets using a well-known video caption benchmark, TRECVid. We train using the MSR-VTT video-caption pairs and we prune this to reduce and make the set of captions describing a video more homogeneously similar, or more diverse, or we prune randomly. We then assess the effectiveness of caption-generating trained with these variations using automatic metrics as well as direct assessment by human assessors. Our findings are preliminary and show that randomly pruning captions from the training data yields the worst performance and that pruning to make the data more homogeneous, or diverse, does improve performance slightly when compared to random. Our work points to the need for more training data, both more video clips but, more importantly, more captions for those videos.
1 aSmeaton, Alan, F.1 aGraham, Yvette1 aMcGuinness, Kevin1 aO'Connor, Noel, E.1 aQuinn, Seán1 aSanchez, Eric, Arazo uhttps://daselab.cs.ksu.edu/publications/exploring-impact-training-data-bias-automatic-generation-video-captions00461nas a2200145 4500008004100000245007000041210006900111260001600180300001200196490000900208100001600217700001900233700002000252856004300272 2019 eng d00aExtensions to the Ontology Design Pattern Representation Language0 aExtensions to the Ontology Design Pattern Representation Languag bCEUR-WS.org a76–750 v24591 aHirt, Quinn1 aShimizu, Cogan1 aHitzler, Pascal uhttp://ceur-ws.org/Vol-2459/short2.pdf01534nas a2200241 4500008004100000245007700041210006900118260004100187520073700228653002000965653001500985653001901000653001301019653002001032653001801052100001901070700002001089700001901109700001601128700002101144700002001165856010701185 2019 eng d00aA Method for Automatically Generating Schema Diagrams for OWL Ontologies0 aMethod for Automatically Generating Schema Diagrams for OWL Onto aVilla Clara, CubabSpringerc06/20193 aInterest in Semantic Web technologies, including knowledge graphs and ontologies, is increasing rapidly in industry and academics. In order to support ontology engineers and domain experts, it is necessary to provide them with robust tools that facilitate the ontology engineering process. Often, the schema diagram of an ontology is the most important tool for quickly conveying the overall purpose of an ontology. In this paper, we present a method for programmatically generating a schema diagram from an OWL file. We evaluate its ability to generate schema diagrams similar to manually drawn schema diagrams and show that it outperforms VOWL and OWLGrEd. In addition, we provide a prototype implementation of this tool.
10adesign patterns10aevaluation10aimplementation10aontology10aschema diagrams10avisualization1 aShimizu, Cogan1 aEberhart, Aaron1 aKarima, Nazifa1 aHirt, Quinn1 aKrisnadhi, Adila1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/method-automatically-generating-schema-diagrams-owl-ontologies00374nas a2200109 4500008004100000245004400041210004300085100001900128700001600147700002000163856008100183 2019 eng d00aMODL: a Modular Ontology Design Library0 aMODL a Modular Ontology Design Library1 aShimizu, Cogan1 aHirt, Quinn1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/modl-modular-ontology-design-library00740nam a2200193 4500008004100000245011200041210006900153260002500222490001000247100002000257700002200277700002400299700002100323700001900344700001900363700001800382700001700400856012900417 2019 eng d00aThe Semantic Web. 16th International Conference, ESWC 2019, Portoroz, Slovenia, June 2-6, 2019, Proceedings0 aSemantic Web 16th International Conference ESWC 2019 Portoroz Sl aHeidelbergbSpringer0 v115031 aHitzler, Pascal1 aFernandez, Miriam1 aJanowicz, Krzysztof1 aZaveri, Amrapali1 aGray, Alasdair1 aLopez, Vanessa1 aHaller, Armin1 aHammar, Karl uhttps://daselab.cs.ksu.edu/publications/semantic-web-16th-international-conference-eswc-2019-portoroz-slovenia-june-2-6-201900866nam a2200241 4500008004100000245011000041210006900151260002500220490001000245100002000255700002100275700001700296700002000313700002400333700002200357700002200379700001700401700002000418700002600438700001600464700002000480856012400500 2019 eng d00aThe Semantic Web: ESWC 2019 Satellite Events. Portoroz, Slovenia, June 2-6, 2019, Revised Selected Papers0 aSemantic Web ESWC 2019 Satellite Events Portoroz Slovenia June 2 aHeidelbergbSpringer0 v117621 aHitzler, Pascal1 aKirrane, Sabrina1 aHartig, Olaf1 ade Boer, Victor1 aVidal, Maria-Esther1 aMaleshkova, Maria1 aSchlobach, Stefan1 aHammar, Karl1 aLasierra, Nelia1 aStadtmüller, Steffen1 aHose, Katja1 aVerborgh, Ruben uhttps://daselab.cs.ksu.edu/publications/semantic-web-eswc-2019-satellite-events-portoroz-slovenia-june-2-6-2019-revised00491nas a2200121 4500008004100000245008100041210006900122260007900191300001600270100001700286700002200303856004400325 2019 eng d00aTowards Architecture-Agnostic Neural Transfer: a Knowledge-Enhanced Approach0 aTowards ArchitectureAgnostic Neural Transfer a KnowledgeEnhanced bInternational Joint Conferences on Artificial Intelligence Organizationc7 a6452–64531 aQuinn, Seán1 aMileo, Alessandra uhttps://doi.org/10.24963/ijcai.2019/91500474nas a2200121 4500008004100000245006200041210006100103260003000164100001300194700002300207700002000230856010200250 2019 eng d00aTowards Association Rule-Based Complex Ontology Alignment0 aTowards Association RuleBased Complex Ontology Alignment aHangzhou, ChinabSpringer1 aZhou, Lu1 aCheatham, Michelle1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/towards-association-rule-based-complex-ontology-alignment02265nas a2200193 4500008004100000245009900041210006900140260003200209520153800241653002801779653002001807653002301827653002401850653002001874100001701894700001701911700002201928856012101950 2019 eng d00aTracking Human Behavioural Consistency by Analysing Periodicity of Household Water Consumption0 aTracking Human Behavioural Consistency by Analysing Periodicity aPrague, Czech RepublicbACM3 aPeople are living longer than ever due to advances in healthcare, and this has prompted many healthcare providers to look towards remote patient care as a means to meet the needs of the future. It is now a priority to enable people to reside in their own homes rather than in overburdened facilities whenever possible. The increasing maturity of IoT technologies and the falling costs of connected sensors has made the deployment of remote healthcare at scale an increasingly attractive prospect. In this work we demonstrate that we can measure the consistency and regularity of the behaviour of a household using sensor readings generated from interaction with the home environment. We show that we can track changes in this behaviour regularity longitudinally and detect changes that may be related to significant life events or trends that may be medically significant. We achieve this using periodicity analysis on water usage readings sampled from the main household water meter every 15 minutes for over 8 months. We utilise an IoT Application Enablement Platform in conjunction with low cost LoRa-enabled sensors and a Low Power Wide Area Network in order to validate a data collection methodology that could be deployed at large scale in future. We envision the statistical methods described here being applied to data streams from the homes of elderly and at-risk groups, both as a means of early illness detection and for monitoring the well-being of those with known illnesses.
10aAmbient Assisted Living10aHome Monitoring10aInternet of Things10aSensor Applications10aSensor Networks1 aQuinn, Seán1 aMurphy, Noel1 aSmeaton, Alan, F. uhttps://daselab.cs.ksu.edu/publications/tracking-human-behavioural-consistency-analysing-periodicity-household-water00555nas a2200169 4500008004100000245008100041210006900122490001900191100002100210700001800231700001900249700002000268700002100288700002000309700002000329856003600349 2018 eng d00aCaregiver Assessment Using Smart Gaming Technology: {A} Preliminary Approach0 aCaregiver Assessment Using Smart Gaming Technology A Preliminary0 vabs/1802.030511 aGoodman, Garrett1 aEdwards, Abby1 aShimizu, Cogan1 aBanerjee, Tanvi1 aHughes, Jennifer1 aRomine, William1 aLawhorne, Larry uhttp://arxiv.org/abs/1802.0305100452nas a2200133 4500008004100000245005100041210004800092260001300140100001300153700002300166700002100189700002000210856008800230 2018 eng d00aA Complex Alignment Benchmark: Geolink dataset0 aComplex Alignment Benchmark Geolink dataset bSpringer1 aZhou, Lu1 aCheatham, Michelle1 aKrisnadhi, Adila1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/complex-alignment-benchmark-geolink-dataset00481nas a2200133 4500008003900000245006200039210005800101100002100159700002300180700002000203700002000223700001300243856009100256 2018 d00aThe First Version of the OAEI Complex Alignment Benchmark0 aFirst Version of the OAEI Complex Alignment Benchmark1 aThieblin, Elodie1 aCheatham, Michelle1 aTrojahn, Cassia1 aZamazal, Ondrej1 aZhou, Lu uhttps://daselab.cs.ksu.edu/publications/first-version-oaei-complex-alignment-benchmark00586nas a2200121 4500008004100000245015200041210006900193260001400262100001900276700002000295700001700315856013200332 2018 eng d00aFormal Ontology in Information Systems - Proceedings of the 10th International Conference, FOIS 2018, Cape Town, South Africa, 19-21 September 20180 aFormal Ontology in Information Systems Proceedings of the 10th I bIOS Press1 aBorgo, Stefano1 aHitzler, Pascal1 aKutz, Oliver uhttps://daselab.cs.ksu.edu/publications/formal-ontology-information-systems-proceedings-10th-international-conference-fois-201800523nas a2200181 4500008004100000245003200041210002800073100002300101700002100124700002000145700002000165700002400185700001900209700001600228700001600244700001300260856006800273 2018 eng d00aThe GeoLink Knowledge Graph0 aGeoLink Knowledge Graph1 aCheatham, Michelle1 aKrisnadhi, Adila1 aAmini, Reihaneh1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aShepherd, Adam1 aNarock, Tom1 aJones, Matt1 aJi, Peng uhttps://daselab.cs.ksu.edu/publications/geolink-knowledge-graph00525nas a2200181 4500008004100000245003200041210002800073100002300101700002100124700002000145700002000165700002400185700001900209700001600228700001600244700001300260856007000273 2018 eng d00aThe GeoLink Knowledge Graph0 aGeoLink Knowledge Graph1 aCheatham, Michelle1 aKrisnadhi, Adila1 aAmini, Reihaneh1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aShepherd, Adam1 aNarock, Tom1 aJones, Matt1 aJi, Peng uhttps://daselab.cs.ksu.edu/publications/geolink-knowledge-graph-001347nas a2200469 4500008004100000245007100041210006900112100001700181700002000198700001900218700002200237700001600259700001500275700002300290700002300313700001700336700001500353700001600368700001600384700001600400700002000416700001600436700001700452700002300469700001300492700001700505700002300522700001900545700001600564700001600580700001600596700001900612700001800631700001700649700002000666700001900686700002000705700001800725700001700743700001500760856010200775 2018 eng d00aIntelligent Systems for Geosciences - An Essential Research Agenda0 aIntelligent Systems for Geosciences An Essential Research Agenda1 aGil, Yolanda1 aPierce, Suzanne1 aBabaie, Hassan1 aBanerjee, Arindam1 aBorne, Kirk1 aBust, Gary1 aCheatham, Michelle1 aEbert-Uphoff, Imme1 aGomes, Carla1 aHill, Mary1 aHorel, John1 aHsu, Leslie1 aKinter, Jim1 aKnoblock, Craig1 aKrum, David1 aKumar, Vipin1 aLermusiaux, Pierre1 aLiu, Yan1 aNorth, Chris1 aPankratius, Victor1 aPeters, Shanan1 aPlale, Beth1 aPope, Allen1 aRavela, Sai1 aRestrepo, Juan1 aRidley, Aaron1 aSamet, Hanan1 aShekhar, Shashi1 aSkinner, Katie1 aSmyth, Padhraic1 aTikoff, Basil1 aYarmey, Lynn1 aZhang, Jia uhttps://daselab.cs.ksu.edu/publications/intelligent-systems-geosciences-essential-research-agenda00380nas a2200085 4500008004100000245007200041210006900113100001300182856009900195 2018 eng d00aA Journey From Simple to Complex Alignment on Real-World Ontologies0 aJourney From Simple to Complex Alignment on RealWorld Ontologies1 aZhou, Lu uhttps://daselab.cs.ksu.edu/publications/journey-simple-complex-alignment-real-world-ontologies02311nas a2200241 4500008004100000022001400041245006800055210006700123300001200190490000600202520156900208653002301777653002101800653001501821653001501836100003401851700002501885700002901910700001801939700002001957700002001977856007201997 2018 eng d a2352-864800aMachine learning for internet of things data analysis: a survey0 aMachine learning for internet of things data analysis a survey a161-1750 v43 aRapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration.
10aInternet of Things10aMachine learning10aSmart City10aSmart data1 aMahdavinejad, Mohammad, Saeid1 aRezvan, Mohammadreza1 aBarekatain, Mohammadamin1 aAdibi, Peyman1 aBarnaghi, Payam1 aSheth, Amit, P. uhttps://www.sciencedirect.com/science/article/pii/S235286481730247X00547nas a2200157 4500008004100000245004900041210004900090300001200139100002800151700003400179700002000213700003200233700002300265700001600288856008500304 2018 eng d00aMental Health Analysis Via Social Media Data0 aMental Health Analysis Via Social Media Data a459-4601 aYazdavar, Amir, Hossein1 aMahdavinejad, Mohammad, Saied1 aBajaj, Goonmeet1 aThirunarayan, Krishnaprasad1 aPathak, Jyotishman1 aSheth, Amit uhttps://daselab.cs.ksu.edu/publications/mental-health-analysis-social-media-data00569nas a2200145 4500008004100000245006500041210006400106100002800170700003400198700002000232700003200252700002300284700001600307856010000323 2018 eng d00aMental Health Analysis Via Social Media Data, IEEE ICHI 20180 aMental Health Analysis Via Social Media Data IEEE ICHI 20181 aYazdavar, Amir, Hossein1 aMahdavinejad, Mohammad, Saied1 aBajaj, Goonmeet1 aThirunarayan, Krishnaprasad1 aPathak, Jyotishman1 aSheth, Amit uhttps://daselab.cs.ksu.edu/publications/mental-health-analysis-social-media-data-ieee-ichi-201800441nas a2200133 4500008004100000245007600041210006900117260001300186300001000199490001000209100002000219700001900239856004900258 2018 eng d00aModular Ontologies as a Bridge Between Human Conceptualization and Data0 aModular Ontologies as a Bridge Between Human Conceptualization a bSpringer a3–60 v108721 aHitzler, Pascal1 aShimizu, Cogan uhttps://doi.org/10.1007/978-3-319-91379-7\_100472nas a2200145 4500008004100000245007600041210006900117260001400186300001400200490000700214100001900221700002000240700001600260856005000276 2018 eng d00aOntology Design Patterns for Winston's Taxonomy Of Part-Whole Relations0 aOntology Design Patterns for Winstons Taxonomy Of PartWhole Rela bIOS Press a119–1290 v361 aShimizu, Cogan1 aHitzler, Pascal1 aPaul, Clare uhttps://doi.org/10.3233/978-1-61499-894-5-11900476nas a2200133 4500008004100000245006100041210005700102490000700159100002300166700002000189700002200209700002400231856008700255 2018 eng d00aThe Properties of Property Alignment on the Semantic Web0 aProperties of Property Alignment on the Semantic Web0 v131 aCheatham, Michelle1 aPesquita, Catia1 aOliveira, Daniela1 aMcCurdy, Helena, B. uhttps://daselab.cs.ksu.edu/publications/properties-property-alignment-semantic-web00713nas a2200181 4500008004100000245011200041210006900153490000600222100002400228700001900252700002000271700001800291700002500309700002300334700002300357700002000380856013100400 2018 eng d00aOn the Prospects of Blockchain and Distributed Ledger Technologies for Open Science and Academic Publishing0 aProspects of Blockchain and Distributed Ledger Technologies for 0 v91 aJanowicz, Krzysztof1 aRegalia, Blake1 aHitzler, Pascal1 aMai, Gengchen1 aDelbecque, Stephanie1 aFröhlich, Maarten1 aMertinent, Patrick1 aLazarus, Trevor uhttp://www.semantic-web-journal.net/content/prospects-blockchain-and-distributed-ledger-technologies-open-science-and-academic00500nas a2200145 4500008004100000245006400041210006100105260001300166300001200179490001000191100001900201700001600220700002000236856009800256 2018 eng d00aA Protégé Plug-In for Annotating OWL Ontologies with OPLa0 aProtégé PlugIn for Annotating OWL Ontologies with OPLa bSpringer a23–270 v111551 aShimizu, Cogan1 aHirt, Quinn1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/prot%C3%A9g%C3%A9-plug-annotating-owl-ontologies-opla00883nas a2200229 4500008004100000020002200041245004000063210003900103260003900142300001200181490001000193520026000203653000800463653002200471653000700493653002200500653001900522100002000541700002300561700002000584856004900604 2018 eng d a978-3-319-98191-800aPseudo-Random ALC Syntax Generation0 aPseudoRandom ALC Syntax Generation aHeraklion, Crete, GreecebSpringer a19–220 v111553 aWe discuss a tool capable of rapidly generating pseudo-random syntactically valid ALC expression trees. The program is meant to allow a researcher to create large sets of independently valid expressions with a minimum of personal bias for experimentation.10aALC10aDescription Logic10aDL10arandom generation10asynthetic data1 aEberhart, Aaron1 aCheatham, Michelle1 aHitzler, Pascal uhttps://doi.org/10.1007/978-3-319-98192-5\_401698nas a2200157 4500008004100000245005900041210005900100520122200159100002201381700002701403700002201430700001401452700001701466700002001483856003701503 2018 eng d00aReasoning over RDF Knowledge Bases using Deep Learning0 aReasoning over RDF Knowledge Bases using Deep Learning3 aSemantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular the scalability issues arising from the ever-increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.
1 aEbrahimi, Monireh1 aSarker, Md Kamruzzaman1 aBianchi, Federico1 aXie, Ning1 aDoran, Derek1 aHitzler, Pascal uhttps://arxiv.org/abs/1811.0413200344nas a2200097 4500008004100000245005900041210005900100100001900159700002000178856004800198 2018 eng d00aSome Open Issues After Twenty Years of Formal Ontology0 aSome Open Issues After Twenty Years of Formal Ontology1 aBorgo, Stefano1 aHitzler, Pascal uhttp://ebooks.iospress.nl/publication/5023600394nas a2200121 4500008004100000245006600041210006400107260001600171300001200187490000900199100001900208856004500227 2018 eng d00aTowards a Comprehensive Modular Ontology {IDE} and Tool Suite0 aTowards a Comprehensive Modular Ontology IDE and Tool Suite bCEUR-WS.org a65–720 v21811 aShimizu, Cogan uhttp://ceur-ws.org/Vol-2181/paper-08.pdf00470nas a2200145 4500008004100000245007200041210006900113260001600182300001200198490000900210100001900219700001700238700001600255856005300271 2018 eng d00aTowards a Pattern-Based Ontology for Chemical Laboratory Procedures0 aTowards a PatternBased Ontology for Chemical Laboratory Procedur bCEUR-WS.org a40–510 v21951 aShimizu, Cogan1 aMcEwen, Leah1 aHirt, Quinn uhttp://ceur-ws.org/Vol-2195/research_paper_1.pdf00486nas a2200097 4500008004100000245010400041210006900145100002000214700002500234856012900259 2018 eng d00aA Tutorial on Modular Ontology Modeling with Ontology Design Patterns: The Cooking Recipes Ontology0 aTutorial on Modular Ontology Modeling with Ontology Design Patte1 aHitzler, Pascal1 aKrisnadhi, Adila, A. uhttps://daselab.cs.ksu.edu/publications/tutorial-modular-ontology-modeling-ontology-design-patterns-cooking-recipes-ontology00542nam a2200169 4500008004100000245004500041210004500086260002500131490000700156100001700163700002000180700002700200700002100227700002200248700002000270856008200290 2017 eng d00aAdvances in Ontology Design and Patterns0 aAdvances in Ontology Design and Patterns aAmsterdambIOS Press0 v321 aHammar, Karl1 aHitzler, Pascal1 aLawrynowicz, Agnieszka1 aKrisnadhi, Adila1 aNuzzolese, Andrea1 aSolanki, Monika uhttps://daselab.cs.ksu.edu/publications/advances-ontology-design-and-patterns01271nas a2200121 4500008004100000245005600041210005600097520083900153100002200992700002801014700001601042856009101058 2017 eng d00aChallenges of Sentiment Analysis for Dynamic Events0 aChallenges of Sentiment Analysis for Dynamic Events3 aEfforts to assess people's sentiments on Twitter have suggested that Twitter could be a valuable resource for studying political sentiment and that it reflects the offline political landscape. Many opinion mining systems and tools provide users with people's attitudes toward products, people, or topics and their attributes/aspects. However, although it may appear simple, using sentiment analysis to predict election results is difficult, since it is empirically challenging to train a successful model to conduct sentiment analysis on tweet streams for a dynamic event such as an election. This article highlights some of the challenges related to sentiment analysis encountered during monitoring of the presidential election using Kno.e.sis's Twitris system.
1 aEbrahimi, Monireh1 aYazdavar, Amir, Hossein1 aSheth, Amit uhttps://daselab.cs.ksu.edu/publications/challenges-sentiment-analysis-dynamic-events-000425nas a2200109 4500008004100000245006300041210005600104100002200160700002800182700001600210856008900226 2017 eng d00aOn the Challenges of Sentiment Analysis for Dynamic Events0 aChallenges of Sentiment Analysis for Dynamic Events1 aEbrahimi, Monireh1 aYazdavar, Amir, Hossein1 aSheth, Amit uhttps://daselab.cs.ksu.edu/publications/challenges-sentiment-analysis-dynamic-events00539nas a2200121 4500008004100000245009500041210006900136100002300205700002200228700001900250700002000269856012800289 2017 eng d00aComputational Environment: An ODP to Support Finding and Recreating Computational Analyses0 aComputational Environment An ODP to Support Finding and Recreati1 aCheatham, Michelle1 aVardeman, Charles1 aKarima, Nazifa1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/computational-environment-odp-support-finding-and-recreating-computational-analyses00343nas a2200109 4500008004100000245003000041210002800071260002500099100002500124700002000149856006400169 2017 eng d00aA Core Pattern for Events0 aCore Pattern for Events aAmsterdambIOS Press1 aKrisnadhi, Adila, A.1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/core-pattern-events01111nas a2200169 4500008004100000245007600041210006900117260003600186300000700222490003100229520052800260653002200788653002900810653001400839100001800853856007000871 2017 eng d00aEfficient Reasoning Algorithms for Fragments of Horn Description Logics0 aEfficient Reasoning Algorithms for Fragments of Horn Description aDaytonbWright State University a700 vDoctor of Philosophy (PhD)3 aWe characterize two fragments of Horn Description Logics and we define two specialized reasoning algorithms that effectively solve the standard reasoning tasks over each of such fragments. We believe our work to be of general interest since (1) a rather large proportion of real-world Horn ontologies belong to some of these two fragments and (2) the implementations based on our reasoning approach significantly outperform state-of-the-art reasoners. Claims (1) and (2) are extensively proven via empirically evaluation. 10aDescription Logic10aKnowledge representation10aReasoning1 aCarral, David uhttp://rave.ohiolink.edu/etdc/view?acc_num=wright149131709653093800988nas a2200181 4500008004100000245008300041210006900124250000700193260002400200520041000224653002800634100002700662700001400689700001700703700002000720700002000740856004600760 2017 eng d00aExplaining Trained Neural Networks with Semantic Web Technologies: First Steps0 aExplaining Trained Neural Networks with Semantic Web Technologie a12 aLondon, UKc07/20173 aThe ever increasing prevalence of publicly available structured data on the World Wide Web enables new applications in a variety of domains. In this paper, we provide a conceptual approach that leverages such data in order to explain the input-output behavior of trained artificial neural networks. We apply existing Semantic Web technologies in order to provide an experimental proof of concept.
10aArtificial Intelligence1 aSarker, Md Kamruzzaman1 aXie, Ning1 aDoran, Derek1 aRaymer, Michael1 aHitzler, Pascal uhttp://daselab.cs.wright.edu/nesy/NeSy17/00387nas a2200121 4500008004100000245004100041210003400082100001800116700002000134700001700154700002300171856007100194 2017 eng d00aOn the Ontological Modeling of Trees0 aOntological Modeling of Trees1 aCarral, David1 aHitzler, Pascal1 aLapp, Hilmar1 aRudolph, Sebastian uhttps://daselab.cs.ksu.edu/publications/ontological-modeling-trees00661nas a2200181 4500008004100000245008500041210006900126300000800195490000600203100002200209700002100231700002300252700002400275700002000299700002000319700002200339856011800361 2017 eng d00aAn Ontology Design Pattern and Its Use Case for Modeling Material Transformation0 aOntology Design Pattern and Its Use Case for Modeling Material T a7310 v81 aVardeman, Charles1 aKrisnadhi, Adila1 aCheatham, Michelle1 aJanowicz, Krzysztof1 aFerguson, Holly1 aHitzler, Pascal1 aBuccellato, Aimee uhttps://daselab.cs.ksu.edu/publications/ontology-design-pattern-and-its-use-case-modeling-material-transformation01271nas a2200109 4500008004100000245005300041210005000094520088900144100001901033700002301052856008601075 2017 eng d00aAn Ontology Design Pattern for Microblog Entries0 aOntology Design Pattern for Microblog Entries3 aDue to the exponential growth of the Internet of Things and use of Social Media Platforms, observers have an unprecedented level of detailed information available on the behavior of communities. However, due to the highly heterogeneous nature and the immense volume of the data, a composite view is difficult to generate. Such a composite view would be exceptionally useful in the realms of insider threat detection, after-action forensics, and hazardous situation detection and avoidance. The Semantic Web, via ontology modeling, offers a powerful tool for fusing the disparate data sources and formats. To this end, we have created an ontology design pattern (ODP) for the modeling of a simple microblog entry. This ODP is intended to fit within an ecosystem for fusing social media, support advanced visualization, and provide a preliminary framework for trust assessment.
1 aShimizu, Cogan1 aCheatham, Michelle uhttps://daselab.cs.ksu.edu/publications/ontology-design-pattern-microblog-entries01379nas a2200157 4500008004100000245008200041210006900123260001200192300000700204490001100211520082100222100001801043700002001061700002301081856011701104 2017 eng d00aPropositional Rule Extraction from Neural Networks under Background Knowledge0 aPropositional Rule Extraction from Neural Networks under Backgro c07/2017 a500 vMaster3 aIt is well-known that the input-output behaviour of a neural network can be recast in terms of a set of propositional rules, and under certain weak preconditions this is also always possible with positive (or definite) rules. Furthermore, in this case there is in fact a unique minimal (technically, reduced) set of such rules which perfectly captures the inputoutput mapping. In this paper, we investigate to what extent these results and corresponding rule extraction algorithms can be lifted to take additional background knowledge into account. It turns out that uniqueness of the solution can then no longer be guaranteed. However, the background knowledge often makes it possible to extract simpler, and thus more easily understandable, rulesets which still perfectly capture the input-output mapping.
1 aLabaf, Maryam1 aHitzler, Pascal1 aEvans, Anthony, B. uhttps://daselab.cs.ksu.edu/publications/propositional-rule-extraction-neural-networks-under-background-knowledge00643nas a2200169 4500008004100000245008300041210006900124260001200193653002500205653001900230653002400249653002000273100001800293700002000311700002300331856011900354 2017 eng d00a Propositional rule extraction from neural networks under background knowledge0 aPropositional rule extraction from neural networks under backgro c07/201710aBackground knowledge10aNeural Network10aPropositional Logic10aRule Extraction1 aLabaf, Maryam1 aHitzler, Pascal1 aEvans, Anthony, B. uhttps://daselab.cs.ksu.edu/publications/propositional-rule-extraction-neural-networks-under-background-knowledge-001585nas a2200145 4500008004100000245004900041210004700090520110000137100002101237700002801258700003201286700001601318700001601334856008901350 2017 eng d00aRelatedness-based Multi-Entity Summarization0 aRelatednessbased MultiEntity Summarization3 aRepresenting world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple’s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-ofthe-art entity summarization approaches.
1 aGunaratna, Kalpa1 aYazdavar, Amir, Hossein1 aThirunarayan, Krishnaprasad1 aSheth, Amit1 aCheng, Gong uhttps://daselab.cs.ksu.edu/publications/relatedness-based-multi-entity-summarization01637nas a2200157 4500008004100000245007000041210006900111260002700180520106700207100001401274700002701288700001701315700002001332700002001352856010701372 2017 eng d00aRelating Input Concepts to Convolutional Neural Network Decisions0 aRelating Input Concepts to Convolutional Neural Network Decision aCA, USAbNIPSc12/20173 aMany current methods to interpret convolutional neural networks (CNNs) use visualization techniques and words to highlight concepts of the input seemingly relevant to a CNN’s decision. The methods hypothesize that the recognition of these concepts are instrumental in the decision a CNN reaches, but the nature of this relationship has not been well explored. To address this gap, this paper examines the quality of a concept’s recognition by a CNN and the degree to which the recognitions are associated with CNN decisions. The study considers a CNN trained for scene recognition over the ADE20k dataset. It uses a novel approach to find and score the strength of minimally distributed representations of input concepts (defined by objects in scene images) across late stage feature maps. Subsequent analysis finds evidence that concept recognition impacts decision making. Strong recognition of concepts frequently-occurring in few scenes are indicative of correct decisions, but recognizing concepts common to many scenes may mislead the network.
1 aXie, Ning1 aSarker, Md Kamruzzaman1 aDoran, Derek1 aHitzler, Pascal1 aRaymer, Michael uhttps://daselab.cs.ksu.edu/publications/relating-input-concepts-convolutional-neural-network-decisions00426nas a2200121 4500008004100000245004600041210004600087260002500133100001900158700002000177700002200197856008500219 2017 eng d00aRendering OWL in Description Logic Syntax0 aRendering OWL in Description Logic Syntax aHeidelbergbSpringer1 aShimizu, Cogan1 aHitzler, Pascal1 aHorridge, Matthew uhttps://daselab.cs.ksu.edu/publications/rendering-owl-description-logic-syntax-001156nas a2200133 4500008004100000245007800041210006900119260005100188300000800239490002200247520063100269100001900900856010300919 2017 eng d00aRendering OWL in LaTeX for Improved Readability: Extensions to the OWLAPI0 aRendering OWL in LaTeX for Improved Readability Extensions to th aDayton, OhiobWright State Universityc08/2017 a1060 vMaster of Science3 aAs ontology engineering is inherently a multidisciplinary process, it is necessary to utilize multiple vehicles to present an ontology to a user. In order to examine the content of an ontology, formal logic renderings of the axioms appear to be a very helpful approach for some. This thesis introduces a number of incremental improvements to the OWLAPI's \LaTeX{} rendering framework in order to improve the readability, concision, and correctness of OWL files translated into Description Logic and First Order Logic. In addition, we examine the efficacy of these renderings as vehicles for understanding an ontology.
1 aShimizu, Cogan uhttps://daselab.cs.ksu.edu/publications/rendering-owl-latex-improved-readability-extensions-owlapi00948nas a2200109 4500008004500000245008000045210006900125520054300194100001300737700002300750856006500773 2017 Engldsh 00aA Replication Study: Understanding What Drives the Performance in WikiMatch0 aReplication Study Understanding What Drives the Performance in W3 aWe replicate and demonstrate that the performance of the WikiMatch automated ontology alignment system may be driven not by the particular information from Wikipedia directly used by the system, but rather by string similarity and Wikipedia’s manually curated synonym sets, as encoded in the site’s query resolution and page redirection system. In order to gain a detailed understanding of how Wikipedia contributes to WikiMatch, we replicate results reported for WikiMatch and analyze the results to evaluate our hypothesis.
1 aZhou, Lu1 aCheatham, Michelle uhttp://disi.unitn.it/~pavel/om2017/papers/om2017_poster5.pdf01322nas a2200133 4500008004100000245005600041210005500097520085900152100002701011700002101038700001801059700002001077856009101097 2017 eng d00aRule-based OWL Modeling with ROWLTab Protege Plugin0 aRulebased OWL Modeling with ROWLTab Protege Plugin3 aIt has been argued that it is much easier to convey logi- cal statements using rules rather than OWL (or description logic (DL)) axioms. Based on recent theoretical developments on transformations between rules and DLs, we have developed ROWLTab, a Prot ́eg ́e plugin that allows users to enter OWL axioms by way of rules; the plugin then automatically converts these rules into OWL 2 DL axioms if possible, and prompts the user in case such a conversion is not possible without weakening the semantics of the rule. In this paper, we present ROWLTab, together with a user evaluation of its effectiveness compared to entering axioms using the standard Prot ́eg ́e interface. Our evaluation shows that modeling with ROWLTab is much quicker than the standard interface, while at the same time, also less prone to errors for hard modeling tasks.
1 aSarker, Md Kamruzzaman1 aKrisnadhi, Adila1 aCarral, David1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/rule-based-owl-modeling-rowltab-protege-plugin01762nas a2200181 4500008004100000245008800041210006900129520113900198100002801337700002701365700002201392700002001414700002001434700003201454700002301486700001601509856005501525 2017 eng d00aSemi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media0 aSemiSupervised Approach to Monitoring Clinical Depressive Sympto3 aWith the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.
1 aYazdavar, Amir, Hossein1 aAl-Olimat, Hussein, S.1 aEbrahimi, Monireh1 aBajaj, Goonmeet1 aBanerjee, Tanvi1 aThirunarayan, Krishnaprasad1 aPathak, Jyotishman1 aSheth, Amit uhttps://dl.acm.org/doi/abs/10.1145/3110025.312302800677nas a2200169 4500008004100000245008800041210006900129100002800198700002600226700002200252700002000274700002000294700003200314700002300346700001600369856012200385 2017 eng d00aSemi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media0 aSemiSupervised Approach to Monitoring Clinical Depressive Sympto1 aYazdavar, Amir, Hossein1 aAl-Olimat, Hussein, S1 aEbrahimi, Monireh1 aBajaj, Goonmeet1 aBanerjee, Tanvi1 aThirunarayan, Krishnaprasad1 aPathak, Jyotishman1 aSheth, Amit uhttps://daselab.cs.ksu.edu/publications/semi-supervised-approach-monitoring-clinical-depressive-symptoms-social-media00491nas a2200121 4500008004100000245006700041210006500108260002500173100002500198700002000223700002400243856010200267 2017 eng d00aA Spatiotemporal Extent Pattern based on Semantic Trajectories0 aSpatiotemporal Extent Pattern based on Semantic Trajectories aAmsterdambIOS Press1 aKrisnadhi, Adila, A.1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttps://daselab.cs.ksu.edu/publications/spatiotemporal-extent-pattern-based-semantic-trajectories00328nas a2200109 4500008004100000245002500041210002100066260002500087100002500112700002000137856006100157 2017 eng d00aThe Stub Metapattern0 aStub Metapattern aAmsterdambIOS Press1 aKrisnadhi, Adila, A.1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/stub-metapattern00549nas a2200133 4500008004100000245008000041210006900121100002000190700001800210700002400228700002500252700002400277856011400301 2017 eng d00aTowards a simple but useful ontology design pattern representation language0 aTowards a simple but useful ontology design pattern representati1 aHitzler, Pascal1 aGangemi, Aldo1 aJanowicz, Krzysztof1 aKrisnadhi, Adila, A.1 aPresutti, Valentina uhttps://daselab.cs.ksu.edu/publications/towards-simple-useful-ontology-design-pattern-representation-language00413nas a2200145 4500008004100000245002900041210002900070490000700099100002300106700002000129700001800149700001500167700002000182856006500202 2016 eng d00aAI for Traffic Analytics0 aAI for Traffic Analytics0 v171 aMutharaju, Raghava1 aLécué, Freddy1 aPan, Jeff, Z.1 aWu, Jiewen1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/ai-traffic-analytics00880nas a2200265 4500008004100000245006900041210006900110260002500179100001700204700001900221700001800240700002100258700002000279700001800299700002900317700002000346700002400366700001900390700002100409700001600430700001900446700002000465700002000485856010900505 2016 eng d00aCollected Research Questions Concerning Ontology Design Patterns0 aCollected Research Questions Concerning Ontology Design Patterns aAmsterdambIOS Press1 aHammar, Karl1 aBlomqvist, Eva1 aCarral, David1 avan Erp, Marieke1 aFokkens, Antske1 aGangemi, Aldo1 avan Hage, Willem, Robert1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aKarima, Nazifa1 aKrisnadhi, Adila1 aNarock, Tom1 aSegers, Roxane1 aSolanki, Monika1 aSvatek, Vojtech uhttps://daselab.cs.ksu.edu/publications/collected-research-questions-concerning-ontology-design-patterns00550nas a2200169 4500008004100000245005400041210005400095300000800149490000600157100001900163700002000182700002400202700002100226700001900247700002000266856009400286 2016 eng d00aConsiderations regarding Ontology Design Patterns0 aConsiderations regarding Ontology Design Patterns a1-70 v71 aBlomqvist, Eva1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aKrisnadhi, Adila1 aNarock, Thomas1 aSolanki, Monika uhttps://daselab.cs.ksu.edu/publications/considerations-regarding-ontology-design-patterns01431nas a2200133 4500008003900000245010100039210006900140520088300209100001801092700002201110700001801132700002001150856012701170 2016 d00aThe Detector Final State pattern: Using the Web Ontology Language to describe a Physics Analysis0 aDetector Final State pattern Using the Web Ontology Language to 3 aThe Data and Software Preservation for Open Science (DASPOS) collaboration has developed an ontology for describing particle physics analyses. The ontology, a series of data triples, is designed to describe dataset, selection cuts, and measured quantities for an analysis. The ontology specification, written in the Web Ontology Language (OWL), is designed to be interpreted by many pre-existing tools, including search engines, and to apply to both theory and experiment published papers. This paper gives an introduction to OWL and this branch of library science from a particle physicist’s point of view, specifics of the Detector Final State Pattern, and how it is designed to be used in the field of particle physics primarily to archive and recall analyses. A general introduction to DASPOS and how its other work fits in with this topic will also be described.
1 aWatts, Gordon1 aVardeman, Charles1 aCarral, David1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/detector-final-state-pattern-using-web-ontology-language-describe-physics-analysis01027nas a2200121 4500008004100000245007700041210006900118260004500187490000800232520053500240100001800775856011200793 2016 eng d00aEfficient Reasoning Algorithms for Fragments of Horn Description Logics0 aEffi cient Reasoning Algorithms for Fragments of Horn Descriptio aDayton, OH, USAbWright State University0 vPhD3 aWe characterize two fragments of Horn Description Logics and we define two specialized reasoning algorithms that effectively solve the standard reasoning tasks over each of such fragments. We believe our work to be of general interest since (1) a rather large proportion of real-world Horn ontologies belong to some of these two fragments and (2) the implementations based on our reasoning approach significantly outperform state-of-the-art reasoners. Claims (1) and (2) are extensively proven via empirically evaluation.
1 aCarral, David uhttps://daselab.cs.ksu.edu/publications/effi%0Ecient-reasoning-algorithms-fragments-horn-description-logics00281nas a2200073 4500008004100000245004200041210004200083856008200125 2016 eng d00aFinal State Detector ODP OWL Ontology0 aFinal State Detector ODP OWL Ontology uhttps://daselab.cs.ksu.edu/publications/final-state-detector-odp-owl-ontology00269nas a2200073 4500008004100000245003800041210003800079856007800117 2016 eng d00aFinal State Detector ODP RDF Data0 aFinal State Detector ODP RDF Data uhttps://daselab.cs.ksu.edu/publications/final-state-detector-odp-rdf-data00506nas a2200133 4500008004100000245007000041210006900111300001100180490000600191100002800197700002200225700001800247856010700265 2016 eng d00aFuzzy Based Implicit Sentiment Analysis on Quantitative Sentences0 aFuzzy Based Implicit Sentiment Analysis on Quantitative Sentence a7–180 v31 aYazdavar, Amir, Hossein1 aEbrahimi, Monireh1 aSalim, Naomie uhttps://daselab.cs.ksu.edu/publications/fuzzy-based-implicit-sentiment-analysis-quantitative-sentences00309nas a2200097 4500008004100000245002800041210002800069100002400097700002000121856007000141 2016 eng d00aGeospatial Semantic Web0 aGeospatial Semantic Web1 aJanowicz, Krzysztof1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/geospatial-semantic-web-000333nas a2200109 4500008004100000245002800041210002800069260001400097100002400111700002000135856006800155 2016 eng d00aGeospatial Semantic Web0 aGeospatial Semantic Web bWiley/AAG1 aJanowicz, Krzysztof1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/geospatial-semantic-web00418nas a2200121 4500008004100000245004500041210004500086260002700131100001900158700001700177700002000194856008200214 2016 eng d00aHow to Document Ontology Design Patterns0 aHow to Document Ontology Design Patterns aKobe, JapanbIOS Press1 aKarima, Nazifa1 aHammar, Karl1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/how-document-ontology-design-patterns00523nas a2200145 4500008004100000245005700041210005600098260002500154100002400179700001800203700002000221700002100241700002400262856009100286 2016 eng d00aIntroduction: Ontology Design Patterns in a Nutshell0 aIntroduction Ontology Design Patterns in a Nutshell aAmsterdambIOS Press1 aJanowicz, Krzysztof1 aGangemi, Aldo1 aHitzler, Pascal1 aKrisnadhi, Adila1 aPresutti, Valentina uhttps://daselab.cs.ksu.edu/publications/introduction-ontology-design-patterns-nutshell00507nas a2200121 4500008004100000245008900041210006900130490000600199100001700205700002000222700002400242856011900266 2016 eng d00aLinked Dataset Description Papers at the Semantic Web Journal: A Critical Assessment0 aLinked Dataset Description Papers at the Semantic Web Journal A 0 v71 aHogan, Aidan1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttps://daselab.cs.ksu.edu/publications/linked-dataset-description-papers-semantic-web-journal-critical-assessment00380nas a2200133 4500008004100000245002600041210002500067300001000092100002100102700002300123700001900146700001600165856006500181 2016 eng d00aLinked Ocean Data 2.00 aLinked Ocean Data 20 a69-991 aLeadbetter, Adam1 aCheatham, Michelle1 aShepherd, Adam1 aThomas, Rob uhttps://daselab.cs.ksu.edu/publications/linked-ocean-data-2000446nas a2200133 4500008004100000245004800041210004700089260001300136300001400149100002500163700002000188700001700208856008700225 2016 eng d00aLinkGen: Multipurpose linked data generator0 aLinkGen Multipurpose linked data generator bSpringer a113–1211 aJoshi, Amit, Krishna1 aHitzler, Pascal1 aDong, Guozhu uhttps://daselab.cs.ksu.edu/publications/linkgen-multipurpose-linked-data-generator00352nas a2200109 4500008004100000245003400041210003400075100002300109700002000132700001900152856007100171 2016 eng d00aMatching Instances in GeoLink0 aMatching Instances in GeoLink1 aCheatham, Michelle1 aAmini, Reihaneh1 aPatel, Chandan uhttps://daselab.cs.ksu.edu/publications/matching-instances-geolink01045nas a2200145 4500008004100000245005300041210005200094260007200146520055100218100002700769700001800796700002100814700002000835856004400855 2016 eng d00aModeling OWL with Rules: The ROWL Protege Plugin0 aModeling OWL with Rules The ROWL Protege Plugin aKobe, Japanb15th International Semantic Web Conference (ISWC) 20163 aAbstract. In our experience, some ontology users find it much easier to convey logical statements using rules rather than OWL (or description logic) axioms. Based on recent theoretical developments on transformations between rules and description logics, we develop ROWL, a Proteg´ e plugin that allows users to enter OWL axioms by way of rules; the plugin then automatically converts these rules into OWL DL axioms if possible, and prompts the user in case such a conversion is not possible without weakening the semantics of the rule.
1 aSarker, Md Kamruzzaman1 aCarral, David1 aKrisnadhi, Adila1 aHitzler, Pascal uhttp://ceur-ws.org/Vol-1690/paper92.pdf00498nas a2200133 4500008004100000245007600041210006900117260001400186300001100200490000700211100002100218700002000239856010500259 2016 eng d00aModeling With Ontology Design Patterns: Chess Games As a Worked Example0 aModeling With Ontology Design Patterns Chess Games As a Worked E bIOS Press a3–210 v251 aKrisnadhi, Adila1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/modeling-ontology-design-patterns-chess-games-worked-example01175nas a2200193 4500008004100000245009200041210006900133260001200202520048400214653001100698653002800709653002000737653002000757100002300777700002000800700002200820700001900842856012000861 2016 eng d00aA Modification to the Hazardous Situation ODP to Support Risk Assessment and Mitigation0 aModification to the Hazardous Situation ODP to Support Risk Asse c10/20163 aThe Hazardous Situation ontology design pattern models the consequences of exposure of an object to a hazard. In its current form, the ODP is well suited for representing the consequences of exposure after the fact, which is very useful for applications such as damage assessment and recovery planning. In this work, we present a modification to this pattern that enables it to additionally support proactive questions central to risk assessment and mitigation planning.
10ahazard10aOntology Design Pattern10arisk assessment10arisk mitigation1 aCheatham, Michelle1 aFerguson, Holly1 aCharles, Vardeman1 aShimizu, Cogan uhttps://daselab.cs.ksu.edu/publications/modification-hazardous-situation-odp-support-risk-assessment-and-mitigation00894nas a2200133 4500008004100000245003700041210003700078520048400115100002300599700002000622700002200642700001900664856007700683 2016 eng d00aModified Hazardous Situation ODP0 aModified Hazardous Situation ODP3 aThe Hazardous Situation ontology design pattern models the consequences of exposure of an object to a hazard. In its current form, the ODP is well suited for representing the consequences of exposure after the fact, which is very useful for applications such as damage assessment and recovery planning. In this work, we present a modification to this pattern that enables it to additionally support proactive questions central to risk assessment and mitigation planning.
1 aCheatham, Michelle1 aFerguson, Holly1 aVardeman, Charles1 aShimizu, Cogan uhttps://daselab.cs.ksu.edu/publications/modified-hazardous-situation-odp00431nas a2200097 4500008004100000245007800041210006900119260003400188100002100222856009000243 2016 eng d00aModular Ontology Architecture for Data Integration in the GeoLink Project0 aModular Ontology Architecture for Data Integration in the GeoLin aOntology Summit 2016 (online)1 aKrisnadhi, Adila uhttp://ontologforum.org/index.php?title=ConferenceCall_2016_02_25&oldid=22543#hid1C2C00445nas a2200109 4500008004100000245007800041210006900119300001400188490000700202100002100209856010500230 2016 eng d00aOntology Design Patterns for Data Integration: The {G}eo{L}ink Experience0 aOntology Design Patterns for Data Integration The G eo L ink Exp a267 - 2780 v251 aKrisnadhi, Adila uhttps://daselab.cs.ksu.edu/publications/ontology-design-patterns-data-integration-geolink-experience00571nas a2200169 4500008004100000245005600041210005600097300001400153490000700167100002100174700001900195700002000214700002000234700003100254700002400285856009200309 2016 eng d00aOntology Design Patterns for Linked Data Publishing0 aOntology Design Patterns for Linked Data Publishing a201 - 2320 v251 aKrisnadhi, Adila1 aKarima, Nazifa1 aHitzler, Pascal1 aAmini, Reihaneh1 aRodríguez-Doncel, Víctor1 aJanowicz, Krzysztof uhttps://daselab.cs.ksu.edu/publications/ontology-design-patterns-linked-data-publishing00612nam a2200157 4500008004100000245008500041210006900126260002500195490000800220100002000228700001800248700002400266700002100290700002400311856011900335 2016 eng d00aOntology Engineering with Ontology Design Patterns: Foundations and Applications0 aOntology Engineering with Ontology Design Patterns Foundations a aAmsterdambIOS Press0 v0251 aHitzler, Pascal1 aGangemi, Aldo1 aJanowicz, Krzysztof1 aKrisnadhi, Adila1 aPresutti, Valentina uhttps://daselab.cs.ksu.edu/publications/ontology-engineering-ontology-design-patterns-foundations-and-applications00948nas a2200133 4500008004100000245008300041210006900124260009700193520041200290100002700702700002100729700002000750856004400770 2016 eng d00aOWLAx: A Protege Plugin to Support Ontology Axiomatization through Diagramming0 aOWLAx A Protege Plugin to Support Ontology Axiomatization throug aKobe, Japanb15th International Semantic Web Conference, ISWC2016, Kobe, Japan, October 20163 aAbstract. Once the conceptual overview, in terms of a somewhat informal class diagram, has been designed in the course of engineering an ontology, the process of adding many of the appropriate logical axioms is mostly a routine task. We provide a Prot´eg´e3 plugin which supports this task, together with a visual user interface, based on established methods for ontology design pattern modeling.
1 aSarker, Md Kamruzzaman1 aKrisnadhi, Adila1 aHitzler, Pascal uhttp://ceur-ws.org/Vol-1690/paper83.pdf01161nas a2200133 4500008004100000245008000041210006900121300001200190520071700202100001800919700002000937700002000957856005000977 2016 eng d00aA Practical Acyclicity Notion for Query Answering Over Horn-SRIQ Ontologies0 aPractical Acyclicity Notion for Query Answering Over HornSRIQ On a70–853 aConjunctive query answering over expressive Horn Description Logic ontologies is a relevant and challenging problem which, in some cases, can be addressed by application of the chase algorithm. In this paper, we define a novel acyclicity notion which provides a sufficient condition for termination of the restricted chase over Horn-SRIQ TBoxes. We show that this notion generalizes most of the existing acyclicity conditions (both theoretically and empirically). Furthermore, this new acyclicity notion gives rise to a very efficient reasoning procedure. We provide evidence for this by providing a materialization based reasoner for acyclic ontologies which outperforms other state-of-the-art systems.
1 aCarral, David1 aFeier, Cristina1 aHitzler, Pascal uhttp://dx.doi.org/10.1007/978-3-319-46523-4_500320nas a2200121 4500008004100000245002400041210001900065260001400084300001400098490000700112100002100119856005800140 2016 eng d00aThe {R}ole Patterns0 aR ole Patterns bIOS Press a313–3190 v251 aKrisnadhi, Adila uhttps://daselab.cs.ksu.edu/publications/role-patterns00546nas a2200145 4500008004100000245007000041210006900111260002500180100002000205700001500225700001500240700002300255700002000278856010200298 2016 eng d00aReasoning with Large Scale OWL 2 EL Ontologies Based on MapReduce0 aReasoning with Large Scale OWL 2 EL Ontologies Based on MapReduc aHeidelbergbSpringer1 aZhou, Zhangquan1 aQi, Guilin1 aLiu, Chang1 aMutharaju, Raghava1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/reasoning-large-scale-owl-2-el-ontologies-based-mapreduce00544nas a2200145 4500008004100000245007400041210006900115300001600184490000700200100002200207700002800229700001800257700001800275856010500293 2016 eng d00aRecognition of side effects as implicit-opinion words in drug reviews0 aRecognition of side effects as implicitopinion words in drug rev a1018–10320 v401 aEbrahimi, Monireh1 aYazdavar, Amir, Hossein1 aSalim, Naomie1 aEltyeb, Safaa uhttps://daselab.cs.ksu.edu/publications/recognition-side-effects-implicit-opinion-words-drug-reviews01087nas a2200133 4500008004100000245007400041210006900115520057800184100002200762700002600784700001800810700001800828856010700846 2016 eng d00aRecognition of side effects as implicit-opinion words in drug reviews0 aRecognition of side effects as implicitopinion words in drug rev3 aMany opinion-mining systems and tools have been developed to provide users with the attitudes of people toward entities and their attributes or the overall polarities of documents. In addition, side effects are one of the critical measures used to evaluate a patient’s opinion for a particular drug. However, side effect recognition is a challenging task, since side effects coincide with disease symptoms lexically and syntactically. The purpose of this paper is to extract drug side effects from drug reviews as an integral implicit-opinion words.
1 aEbrahimi, Monireh1 aHosseinYazdavar, Amir1 aSalim, Naomie1 aEltyeb, Safaa uhttps://daselab.cs.ksu.edu/publications/recognition-side-effects-implicit-opinion-words-drug-reviews-000485nas a2200145 4500008004100000245005400041210005200095300001100147100002200158700002100180700001400201700001700215700001600232856009100248 2016 eng d00aRock strength estimation: a PSO-based BP approach0 aRock strength estimation a PSObased BP approach a1–121 aMohamad, Tonnizam1 aArmaghani, Jahed1 aMomeni, E1 aYazdavar, AH1 aEbrahimi, M uhttps://daselab.cs.ksu.edu/publications/rock-strength-estimation-pso-based-bp-approach00413nas a2200109 4500008004100000245005900041210005200100260002500152100002000177700002100197856008500218 2016 eng d00aOn the Roles of Logical Axiomatizations for Ontologies0 aRoles of Logical Axiomatizations for Ontologies aAmsterdambIOS Press1 aHitzler, Pascal1 aKrisnadhi, Adila uhttps://daselab.cs.ksu.edu/publications/roles-logical-axiomatizations-ontologies00312nas a2200097 4500008004100000245003000041210003000071100002300101700002000124856007000144 2016 eng d00aSemantic Data Integration0 aSemantic Data Integration1 aCheatham, Michelle1 aPesquita, Catia uhttps://daselab.cs.ksu.edu/publications/semantic-data-integration00419nas a2200097 4500008004100000245006700041210006700108100001900175700002300194856010400217 2016 eng d00aSemantic Web Enabled Record Linkage Attacks on Anonymized Data0 aSemantic Web Enabled Record Linkage Attacks on Anonymized Data1 aMiracle, Jacob1 aCheatham, Michelle uhttps://daselab.cs.ksu.edu/publications/semantic-web-enabled-record-linkage-attacks-anonymized-data01390nas a2200145 4500008004100000245007500041210006900116260001600185520084500201100002001046700002301066700002001089700002401109856011101133 2016 eng d00aTowards Best Practices for Crowdsourcing Ontology Alignment Benchmarks0 aTowards Best Practices for Crowdsourcing Ontology Alignment Benc aKobe, Japan3 aOntology alignment systems establish the links between ontologies that enable knowledge from various sources and domains to be used by applications in many different ways. Unfortunately, these systems are not perfect. Currently, the results of even the best-performing alignment systems need to be manually verified in order to be fully trusted. Ontology alignment researchers have turned to crowdsourcing platforms such as Amazon's Mechanical Turk to accomplish this. However, there has been little systematic analysis of the accuracy of crowdsourcing for alignment verification and the establishment of best practices. In this work, we analyze the impact of the presentation of the context of potential matches and the way in which the question is presented to workers on the accuracy of crowdsourcing for alignment verification.
1 aAmini, Reihaneh1 aCheatham, Michelle1 aGrzebala, Pawel1 aMcCurdy, Helena, B. uhttps://daselab.cs.ksu.edu/publications/towards-best-practices-crowdsourcing-ontology-alignment-benchmarks00777nas a2200145 4500008004100000020002200041245008900063210006900152260011600221520012500337100001300462700001600475700001500491856012500506 2016 eng d a978-1-4503-4143-100aTweet Properly: Analyzing Deleted Tweets to Understand and Identify Regrettable Ones0 aTweet Properly Analyzing Deleted Tweets to Understand and Identi bInternational World Wide Web Conferences Steering Committee Republic and Canton of Geneva, Switzerlandc04/20163 a
The success of linked data has resulted in a large amount of data being generated in a standard RDF format. Various techniques have been explored to generate a compressed version of RDF datasets for archival and transmission purpose. However, these compression techniques are designed to compress a given dataset without using any external knowledge, either through a compact representation or removal of semantic redundancies present in the dataset. In this paper, we introduce a novel approach to compress RDF datasets by exploiting alignments present across various datasets at both instance and schema level. Our system generates lossy compression based on the confidence value of relation between the terms. We also present a comprehensive evaluation of the approach by using reference alignment from OAEI.
1 aJoshi, Amit, Krishna1 aHitzler, Pascal1 aDong, Guozhu uhttps://daselab.cs.ksu.edu/publications/alignment-aware-linked-data-compression00413nas a2200109 4500008004100000245005500041210005400096260002300150100002300173700002300196856008400219 2015 eng d00aAre We Really Standing on the Shoulders of Giants?0 aAre We Really Standing on the Shoulders of Giants aPortoroz, Slovenia1 aMutharaju, Raghava1 aKapanipathi, Pavan uhttps://daselab.cs.ksu.edu/publications/are-we-really-standing-shoulders-giants01299nas a2200145 4500008004100000245007100041210006700112520079600179100002000975700001800995700002301013700002701036700001801063856007201081 2015 eng d00aThe Combined Approach to Query Answering Beyond the OWL 2 Profiles0 aCombined Approach to Query Answering Beyond the OWL 2 Profiles3 aCombined approaches have become a successful technique for CQ answering over ontologies. Existing algorithms, however, are restricted to the logics underpinning the OWL 2 profiles. Our goal is to make combined approaches applicable to a wider range of ontologies. We focus on RSA: a class of Horn ontologies that extends the profiles while ensuring tractability of standard reasoning. We show that CQ answering over RSA ontologies without role composition is feasible in NP. Our reasoning procedure generalises the combined approach for ELHO and DL-LiteR using an encoding of CQ answering into fact entailment w.r.t. a logic program with function symbols and stratified negation. Our results have significant practical implications since many out-of-profile Horn ontologies are RSA.
1 aFeier, Cristina1 aCarral, David1 aStefanoni, Giorgio1 aGrau, Bernardo, Cuenca1 aHorrocks, Ian uhttp://www.cs.ox.ac.uk/isg/people/cristina.feier/ijcai_rsafinal.pdf00460nas a2200109 4500008003900000245007400039210006900113100002000182700002300202700001900225856010600244 2015 d00aData Perturbation via Randomized Normalization for Privacy Protection0 aData Perturbation via Randomized Normalization for Privacy Prote1 aAmini, Reihaneh1 aCheatham, Michelle1 aKarima, Nazifa uhttps://daselab.cs.ksu.edu/publications/data-perturbation-randomized-normalization-privacy-protection01559nas a2200193 4500008004100000245004600041210004600087260003300133520095200166653001101118653002601129653002801155653001101183100002301194700002001217700002201237700002001259856008601279 2015 eng d00aDistributed and Scalable OWL EL Reasoning0 aDistributed and Scalable OWL EL Reasoning aPortoroz, SloveniabSpringer3 aOWL 2 EL is one of the tractable proles of the Web Ontology Language (OWL) which is a W3C-recommended standard. OWL 2 EL provides sucient expressivity to model large biomedical ontologies as well as streaming data such as trac, while at the same time allows for ecient reasoning services. Existing reasoners for OWL 2 EL, however, use only a single machine and are thus constrained by memory and computational power. At the same time, the automated generation of ontological information from streaming data and text can lead to very large ontologies which can exceed the capacities of these reasoners. We thus describe a distributed reasoning system that scales well using a cluster of commodity machines. We also apply our system to a use case on city trac data and show that it can handle volumes which cannot be handled by current single machine reasoners.
10aDistEL10aDistributed Reasoning10aOntology Classification10aOWL EL1 aMutharaju, Raghava1 aHitzler, Pascal1 aMateti, Prabhaker1 aLécué, Freddy uhttps://daselab.cs.ksu.edu/publications/distributed-and-scalable-owl-el-reasoning00434nas a2200097 4500008004100000245007300041210006900114260001700183100002300200856011300223 2015 eng d00aDistributed Reasoning over Ontology Streams and Large Knowledge Base0 aDistributed Reasoning over Ontology Streams and Large Knowledge aSeattle, USA1 aMutharaju, Raghava uhttps://daselab.cs.ksu.edu/publications/distributed-reasoning-over-ontology-streams-and-large-knowledge-base00925nas a2200289 4500008004100000245006900041210006800110100002100178700002200199700002200221700002300243700001800266700002000284700002400304700001300328700001900341700002100360700002100381700001900402700001600421700002200437700001800459700002200477700001900499700001700518856010000535 2015 eng d00aEarthCube GeoLink: Semantics and Linked Data for the Geosciences0 aEarthCube GeoLink Semantics and Linked Data for the Geosciences1 aArko, Robert, A.1 aCarbotte, Suzanne1 aChandler, Cynthia1 aCheatham, Michelle1 aFils, Douglas1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aJi, Peng1 aJones, Matthew1 aKrisnadhi, Adila1 aLehnert, Kerstin1 aMickle, Audrey1 aNarock, Tom1 aO'Brien, Margaret1 aRaymond, Lisa1 aSchildhauer, Mark1 aShepherd, Adam1 aWiebe, Peter uhttps://daselab.cs.ksu.edu/publications/earthcube-geolink-semantics-and-linked-data-geosciences01289nas a2200145 4500008004100000245007400041210006900115520078400184100002000968700001800988700002301006700002701029700001801056856006901074 2015 eng d00aExtending the Combined Approach Beyond Lightweight Description Logics0 aExtending the Combined Approach Beyond Lightweight Description L3 aCombined approaches have become a successful technique for CQ answering over ontologies. Existing algorithms, however, are restricted to the logics underpinning the OWL 2 profiles. Our goal is to make combined approaches applicable to a wider range of ontologies. We focus on RSA: a class of Horn ontologies that extends the profiles while ensuring tractability of standard reasoning. We show that CQ answering over RSA ontologies without role composition is feasible in NP. Our reasoning procedure generalises the combined approach for ELHO and DL-LiteR using an encoding of CQ answering into fact entailment w.r.t. a logic program with function symbols and stratified negation. Our results are significant in practice since many out-of-profile Horn ontologies are RSA.
1 aFeier, Cristina1 aCarral, David1 aStefanoni, Giorgio1 aGrau, Bernardo, Cuenca1 aHorrocks, Ian uhttp://www.cs.ox.ac.uk/isg/people/cristina.feier/pdfs/dlmain.pdf01032nas a2200337 4500008003900000245007000039210006300109260001200172100002100184700001600205700002400221700002000245700001700265700002200282700002200304700002300326700001800349700001900367700001300386700001900399700001900418700002100437700001900458700001900477700002200496700001800518700001900536700002200555700001700577856010000594 2015 d00aThe {GeoLink} Framework for Pattern-based Linked Data Integration0 aGeoLink Framework for Patternbased Linked Data Integration c10/20151 aKrisnadhi, Adila1 aHu, Yingjie1 aJanowicz, Krzsyztof1 aHitzler, Pascal1 aArko, Robert1 aCarbotte, Suzanne1 aChandler, Cynthia1 aCheatham, Michelle1 aFils, Douglas1 aFinin, Timothy1 aJi, Peng1 aJones, Matthew1 aKarima, Nazifa1 aLehnert, Kerstin1 aMickle, Audrey1 aNarock, Thomas1 aO'Brien, Margaret1 aRaymond, Lisa1 aShepherd, Adam1 aSchildhauer, Mark1 aWiebe, Peter uhttps://daselab.cs.ksu.edu/publications/geolink-framework-pattern-based-linked-data-integration00983nas a2200337 4500008004100000245004800041210004200089260002200131100002100153700001600174700002400190700002000214700001700234700002200251700002200273700002300295700001800318700001900336700001300355700001900368700001900387700002100406700001900427700001900446700002200465700001800487700001900505700002200524700001700546856008200563 2015 eng d00aThe {GeoLink} Modular Oceanography Ontology0 aGeoLink Modular Oceanography Ontology bSpringerc10/20151 aKrisnadhi, Adila1 aHu, Yingjie1 aJanowicz, Krzysztof1 aHitzler, Pascal1 aArko, Robert1 aCarbotte, Suzanne1 aChandler, Cynthia1 aCheatham, Michelle1 aFils, Douglas1 aFinin, Timothy1 aJi, Peng1 aJones, Matthew1 aKarima, Nazifa1 aLehnert, Kerstin1 aMickle, Audrey1 aNarock, Thomas1 aO'Brien, Margaret1 aRaymond, Lisa1 aShepherd, Adam1 aSchildhauer, Mark1 aWiebe, Peter uhttps://daselab.cs.ksu.edu/publications/geolink-modular-oceanography-ontology00500nas a2200133 4500008004500000020002200045245004900067210004900116260007300165100001300238700001600251700001500267856008400282 2015 Engldsh a978-1-4503-3473-000aIdentifying Regrettable Messages from Tweets0 aIdentifying Regrettable Messages from Tweets bInternational World Wide Web Conferences Steering Committeec05/20151 aZhou, Lu1 aWang, Wenbo1 aChen, Keke uhttps://daselab.cs.ksu.edu/publications/identifying-regrettable-messages-tweets03788nas a2200109 4500008004100000245005900041210005600100490002500156520338400181100002003565856009303585 2015 eng d00aA Language for Inconsistency-Tolerant Ontology Mapping0 aLanguage for InconsistencyTolerant Ontology Mapping0 vDoctor of Philosophy3 aOntology alignment plays a key role in enabling interoperability among various data sources present in the web. The nature of the world is such, that the same concepts differ in meaning, often so slightly, which makes it difficult to relate these concepts. It is the omni-present heterogeneity that is at the core of the web. The research work presented in this dissertation, is driven by the goal of providing a robust ontology alignment language for the semantic web, as we show that description logics based alignment languages are not suitable for aligning ontologies. The adoption of the semantic web technologies has been consistently on the rise over the past decade, and it continues to show promise. The core component of the semantic web is the set of knowledge representation languages -- mainly the W3C (World Wide Web Consortium) standards Web Ontology Language (OWL), Resource Description Framework (RDF), and Rule Interchange Format (RIF). While these languages have been designed in order to be suitable for the openness and extensibility of the web, they lack certain features which we try to address in this dissertation. One such missing component is the lack of non-monotonic features, in the knowledge representation languages, that enable us to perform common sense reasoning. For example, OWL supports the open world assumption (OWA), which means that knowledge about everything is assumed to be possibly incomplete at any point of time. However, experience has shown that there are situations that require us to assume that certain parts of the knowledge base are complete. Employing the Closed World Assumption (CWA) helps us achieve this. Circumscription is a very well-known approach towards CWA, which provides closed world semantics by employing the idea of minimal models with respect to certain predicates which are closed. We provide the formal semantics of the notion of Grounded Circumscription, which is an extension of circumscription with desirable properties like decidability. We also provide a tableaux calculus to reason over knowledge bases under the notion of grounded circumscription. Another form of common sense logic, is default logic. Default logic provides a way to specify rules that, by default, hold in most cases but not necessarily in all cases. The classic example of such a rule is: If something is a bird then it flies. The power of defaults comes from the ability of the logic to handle exceptions to the default rules. For example, a bird will be assumed to fly by default unless it is an exception, i.e. it belongs to a class of birds that do not fly, like penguins. Interestingly, this property of defaults can be utilized to create mappings between concepts of different ontologies (knowledge bases). We provide a new semantics for the integration of defaults in description logics and show that it improves upon previously known results in literature. In this study, we give various examples to show the utility and advantages of using a default logic based ontology alignment language. We provide the semantics and decidability results of a default based mapping language for tractable fragments of description logics (or OWL). Furthermore, we provide a proof of concept system and qualitative analysis of the results obtained from the system when compared to that of traditional mapping repair techniques.
1 aSengupta, Kunal uhttps://daselab.cs.ksu.edu/publications/language-inconsistency-tolerant-ontology-mapping00796nas a2200253 4500008004100000245005600041210005500097100001900152700002200171700002100193700001900214700002000233700002400253700002100277700002200298700001800320700001600338700001900354700002200373700002100395700001900416700001900435856008800454 2015 eng d00aLinked Data: Forming Partnerships at the Data Layer0 aLinked Data Forming Partnerships at the Data Layer1 aShepherd, Adam1 aChandler, Cynthia1 aArko, Robert, A.1 aJones, Matthew1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aKrisnadhi, Adila1 aSchildhauer, Mark1 aFils, Douglas1 aNarock, Tom1 aGroman, Robert1 aO'Brien, Margaret1 aPatton, Evan, W.1 aKinkade, Danie1 aRauch, Shannon uhttps://daselab.cs.ksu.edu/publications/linked-data-forming-partnerships-data-layer00813nas a2200253 4500008004100000245006200041210006000103260001200163100002400175700002100199700001600220700001700236700002600253700002000279700002000299700002100319700002100340700002000361700002200381700002300403700002200426700001500448856009600463 2015 eng d00aA Minimal Ontology Pattern for Life Cycle Assessment Data0 aMinimal Ontology Pattern for Life Cycle Assessment Data c10/20151 aJanowicz, Krzysztof1 aKrisnadhi, Adila1 aHu, Yingjie1 aSuh, Sangwon1 aWeidema, Bo, Pedersen1 aRivela, Beatriz1 aTivander, Johan1 aMeyer, David, E.1 aBerg-Cross, Gary1 aHitzler, Pascal1 aIngwersen, Wesley1 aKuczenski, Brandon1 aVardeman, Charles1 aJu, Yiting uhttps://daselab.cs.ksu.edu/publications/minimal-ontology-pattern-life-cycle-assessment-data00737nas a2200205 4500008004100000245007300041210006900114260001500183100003000198700002200228700001800250700002000268700002000288700001800308700002500326700002000351700002500371700002300396856011200419 2015 eng d00aNeural-Symbolic Learning and Reasoning: Contributions and Challenges0 aNeuralSymbolic Learning and Reasoning Contributions and Challeng bAAAI Press1 aGarcez, Artur, S. d'Avila1 aBesold, Tarek, R.1 aDe Raedt, Luc1 aFöldiak, Peter1 aHitzler, Pascal1 aIcard, Thomas1 aKühnberger, Kai-Uwe1 aLamb, Luís, C.1 aMiikkulainen, Riisto1 aSilver, Daniel, L. uhttps://daselab.cs.ksu.edu/publications/neural-symbolic-learning-and-reasoning-contributions-and-challenges00551nas a2200133 4500008004100000245008900041210006900130100001800199700002100217700001800238700002000256700001800276856012300294 2015 eng d00aOntological Support of Data Discovery and Synthesis in Estuarine and Coastal Science0 aOntological Support of Data Discovery and Synthesis in Estuarine1 aThessen, Anne1 aFertig, Benjamin1 aWalls, Ramona1 aHitzler, Pascal1 aZiegler, Rick uhttps://daselab.cs.ksu.edu/publications/ontological-support-data-discovery-and-synthesis-estuarine-and-coastal-science00552nas a2200181 4500008004100000245004700041210004400088260001200132490000900144100002100153700003100174700002000205700002300225700001900248700002000267700002000287856006300307 2015 eng d00aAn Ontology Design Pattern for Chess Games0 aOntology Design Pattern for Chess Games c10/20150 v14611 aKrisnadhi, Adila1 aRodríguez-Doncel, Víctor1 aHitzler, Pascal1 aCheatham, Michelle1 aKarima, Nazifa1 aAmini, Reihaneh1 aColeman, Ashley uhttp://ceur-ws.org/Vol-1461/WOP2015_pattern_abstract_2.pdf00943nas a2200145 4500008004100000245007400041210006900115260001200184520042600196100002000622700001800642700001700660700002000677856010000697 2015 eng d00aAn Ontology Design Pattern for Data Integration in the Library Domain0 aOntology Design Pattern for Data Integration in the Library Doma c10/20153 aA university’s institutional repository (IR) contains the in- tellectual output of its faculty, staff and students. Its content is exten- sive and heterogenous, which complicates data aggregation and discovery tasks. To address these challenges, we propose the use of a conceptual ontology design pattern to model information for the IR domain which is general enough to be reused across different IR datasets.
1 aObrien, Patrick1 aCarral, David1 aMixter, Jeff1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/ontology-design-pattern-data-integration-library-domain00612nas a2200193 4500008004100000245006600041210006300107260002500170490000900195100002000204700002100224700002200245700001900267700002000286700002100306700001900327700002000346856005200366 2015 eng d00aAn Ontology Design Pattern for Dynamic Relative Relationships0 aOntology Design Pattern for Dynamic Relative Relationships bCEUR-WS.orgc10/20150 v14611 aFerguson, Holly1 aKrisnadhi, Adila1 aVardeman, Charles1 aBlomqvist, Eva1 aHitzler, Pascal1 aKrisnadhi, Adila1 aNarock, Thomas1 aSolanki, Monika uhttp://ceur-ws.org/Vol-1461/WOP2015_paper_3.pdf01469nas a2200301 4500008004100000245006100041210005800102260002500160490000900185520055600194100001800750700002300768700002800791700002400819700002600843700002000869700002100889700002500910700003000935700002200965700001800987700001901005700002001024700002101044700001901065700002001084856006301104 2015 eng d00aAn Ontology Design Pattern for Particle Physics Analysis0 aOntology Design Pattern for Particle Physics Analysis bCEUR-WS.orgc10/20150 v14613 aThe detector final state is the core element of particle physics analysis as it defines the physical characteristics that form the basis of the measurement presented in a published paper. Although they are a crucial part of the research process, detector final states are not yet formally described, published in papers or searchable in a convenient way. This paper aims at providing an ontology pattern for the detector final state that can be used as a building block for an ontology covering the whole particle physics analysis life cycle.
1 aCarral, David1 aCheatham, Michelle1 aDallmeir-Tiessen, Sunje1 aHerterich, Patricia1 aHildreth, Michael, D.1 aHitzler, Pascal1 aKrisnadhi, Adila1 aLassila-Perini, Kati1 aSexton-Kennedy, Elizabeth1 aVardeman, Charles1 aWatts, Gordon1 aBlomqvist, Eva1 aHitzler, Pascal1 aKrisnadhi, Adila1 aNarock, Thomas1 aSolanki, Monika uhttp://ceur-ws.org/Vol-1461/WOP2015_pattern_abstract_5.pdf00887nas a2200241 4500008004100000245012400041210006900165100001900234700001700253700002100270700002000291700002400311700002200335700001900357700002300376700002200399700001900421700001800440700001900458700001900477700001800496856013100514 2015 eng d00aOntology Design Patterns: Bridging the Gap Between Local Semantic Use Cases and Large-Scale, Long-Term Data Integration0 aOntology Design Patterns Bridging the Gap Between Local Semantic1 aShepherd, Adam1 aArko, Robert1 aKrisnadhi, Adila1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aChandler, Cynthia1 aNarock, Thomas1 aCheatham, Michelle1 aSchildhauer, Mark1 aJones, Matthew1 aRaymond, Lisa1 aMickle, Audrey1 aFinin, Timothy1 aFils, Douglas uhttps://daselab.cs.ksu.edu/publications/ontology-design-patterns-bridging-gap-between-local-semantic-use-cases-and-large-scale00561nas a2200157 4500008004100000245005900041210005900100100002300159700002200182700002400204700002000228700002100248700002200269700001700291856009500308 2015 eng d00aOntology Design Patterns for Semantically Enriched LCA0 aOntology Design Patterns for Semantically Enriched LCA1 aKuczenski, Brandon1 aIngwersen, Wesley1 aJanowicz, Krzysztof1 aHitzler, Pascal1 aBerg-Cross, Gary1 aVardeman, Charles1 aSuh, Sangwon uhttps://daselab.cs.ksu.edu/publications/ontology-design-patterns-semantically-enriched-lca00846nas a2200277 4500008004100000245007800041210006900119260002500188300001000213490000900223100001200232700001600244700002300260700002400283700002200307700002100329700002000350700001700370700002200387700002100409700002000430700002300450700001900473700001800492856005800510 2015 eng d00aAn Ontology For Specifying Spatiotemporal Scopes in Life Cycle Assessment0 aOntology For Specifying Spatiotemporal Scopes in Life Cycle Asse bCEUR-WS.orgc10/2015 a25-300 v15011 aYan, Bo1 aHu, Yingjie1 aKuczenski, Brandon1 aJanowicz, Krzsyztof1 aBallatore, Andrea1 aKrisnadhi, Adila1 aHitzler, Pascal1 aSuh, Sangwon1 aIngwersen, Wesley1 ad'Amato, Claudia1 aLécué, Freddy1 aMutharaju, Raghava1 aNarock, Thomas1 aWirth, Fabian uhttp://ceur-ws.org/Vol-1501/Diversity2015-paper_4.pdf00308nas a2200085 4500008004100000245004200041210004200083100002000125856007700145 2015 eng d00aOntology modeling with domain experts0 aOntology modeling with domain experts1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/ontology-modeling-domain-experts00449nas a2200109 4500008004100000245006800041210006700109100002000176700002400196700002100220856009800241 2015 eng d00aOntology modeling with domain experts: The GeoVoCamp experience0 aOntology modeling with domain experts The GeoVoCamp experience1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aKrisnadhi, Adila uhttps://daselab.cs.ksu.edu/publications/ontology-modeling-domain-experts-geovocamp-experience01948nas a2200253 4500008004100000245012400041210006900165260001400234300001200248520107400260100002101334700001701355700002201372700002201394700002301416700001901439700002001458700002401478700001901502700001801521700001901539700001701558856011901575 2015 eng d00aOntology Pattern Modeling for Cross-Repository Data Integration in the Ocean Sciences: The Oceanographic Cruise Example0 aOntology Pattern Modeling for CrossRepository Data Integration i bIOS Press a256-2843 aEarthCube is a major effort of the National Science Foundation to establish a next-generation knowledge architecture for the broader geosciences. Data storage, retrieval, access, and reuse are central parts of this new effort. Currently, EarthCube is organized around several building blocks and research coordination networks. OceanLink is a semantics-enabled building block that aims at improving data retrieval and reuse via ontologies, Semantic Web technologies, and Linked Data for the ocean sciences. Cruises, in the sense of research expeditions, are central events for ocean scientists. Consequently, information about these cruises and the involved vessels is of primary interest for oceanographers, and thus, needs to be shared and made retrievable. In this paper, we report the use of a design pattern-centric strategy to model Cruise for OceanLink data integration. We provide a formal axiomatization of the introduced pattern using the Web Ontology Language, explain design choices and discuss the planned deployment and application scenarios of our model.1 aKrisnadhi, Adila1 aArko, Robert1 aCarbotte, Suzanne1 aChandler, Cynthia1 aCheatham, Michelle1 aFinin, Timothy1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aNarock, Thomas1 aRaymond, Lisa1 aShepherd, Adam1 aWiebe, Peter uhttps://daselab.cs.ksu.edu/publications/ontology-pattern-modeling-cross-repository-data-integration-ocean-sciences03243nas a2200133 4500008004100000245004400041210004300085260004500128300000800173490002500181520281700206100002103023856006503044 2015 eng d00aOntology Pattern-Based Data Integration0 aOntology PatternBased Data Integration aDaytonbWright State Universityc12/2015 a2330 vDoctor of Philosophy3 aData integration is concerned with providing a unified access to data residing at multiple sources. Such a unified access is realized by having a global schema and a set of mappings between the global schema and the local schemas of each data source, which specify how user queries at the global schema can be translated into queries at the local schemas. Data sources are typically developed and maintained independently, and thus, highly heterogeneous. This causes difficulties in integration because of the lack of interoperability in the aspect of architecture, data format, as well as syntax and semantics of the data.
This dissertation represents a study on how small, self-contained ontologies, called ontology design patterns, can be employed to provide semantic interoperability in a cross-repository data integration system. The idea of this so-called ontology pattern- based data integration is that a collection of ontology design patterns can act as the global schema that still contains sufficient semantics, but is also flexible and simple enough to be used by linked data providers. On the one side, this differs from existing ontology-based solutions, which are based on large, monolithic ontologies that provide very rich semantics, but enforce too restrictive ontological choices, hence are shunned by many data providers. On the other side, this also differs from the purely linked data based solutions, which do offer simplicity and flexibility in data publishing, but too little in terms of semantic interoperability.
We demonstrate the feasibility of this idea through the actual development of a large scale data integration project involving seven ocean science data repositories from five institutions in the U.S. In addition, we make two contributions as part of this dissertation work, which also play crucial roles in the aforementioned data integration project. First, we develop a collection of more than a dozen ontology design patterns that capture the key notions in the ocean science occurring in the participating data repositories. These patterns contain axiomatization of the key notions and were developed with an intensive involvement from the domain experts. Modeling of the patterns was done in a systematic workflow to ensure modularity, reusability, and flexibility of the whole pattern collection. Second, we propose the so-called pattern views that allow data providers to publish their data in very simple intermediate schema and show that they can greatly assist data providers to publish their data without requiring a thorough understanding of the axiomatization of the patterns.
This paper discusses the relationship between ontology design patterns (ODPs), data models and linked data, proposing a method that simplifies the task of publishing linked data while adhering to good modeling practices that reuse well-studied ODPs. The proposed process simplifies the tasks of the domain experts but preserves the integrity of the design patterns, favoring a well-designed and well documented data model which fosters data reuse. The work is illustrated with a linked dataset of two million chess games, with the key information mapped to other linked datasets and supported by formalized design patterns. This is the first time a chess dataset is presented as linked data, and an insight on its usefulness is given.
1 aRodríguez-Doncel, Víctor1 aKrisnadhi, Adila1 aHitzler, Pascal1 aCheatham, Michelle1 aKarima, Nazifa1 aAmini, Reihaneh1 aHartig, Olaf1 aSequeda, Juan1 aHogan, Aidan uhttp://dase.cs.wright.edu/publications/pattern-based-linked-data-publication-linked-chess-dataset-case00628nas a2200157 4500008004100000245018500041210006900226260003600295490000900331100002100340700002000361700002300381700001600404700001800420856003200438 2015 eng d00aProceedings of the 1st International Diversity++ Workshop co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, USA, October 12, 20150 aProceedings of the 1st International Diversity Workshop colocate aBethlehem, PA, USAbCEUR-WS.org0 v15011 ad'Amato, Claudia1 aLécué, Freddy1 aMutharaju, Raghava1 aNarock, Tom1 aWirth, Fabian uhttp://ceur-ws.org/Vol-150100676nas a2200133 4500008004100000245020700041210006900248100001900317700002000336700002100356700001600377700002000393856012900413 2015 eng d00aProceedings of the 6th Workshop on Ontology and Semantic Web Patterns (WOP 2015) co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pensylvania, USA, October 11, 20150 aProceedings of the 6th Workshop on Ontology and Semantic Web Pat1 aBlomqvist, Eva1 aHitzler, Pascal1 aKrisnadhi, Adila1 aNarock, Tom1 aSolanki, Monika uhttps://daselab.cs.ksu.edu/publications/proceedings-6th-workshop-ontology-and-semantic-web-patterns-wop-2015-co-located-14th01985nas a2200325 4500008004100000245005200041210004400093260002500137300001000162490000900172520104900181100002101230700001701251700002201268700002201290700002301312700002001335700001601355700002401371700001301395700001901408700001901427700001701446700002101463700002001484700002301504700001901527700001801546856009501564 2015 eng d00a{R2R+BCO-DMO} – Linked Oceanographic Datasets0 aR2RBCODMO Linked Oceanographic Datasets bCEUR-WS.orgc10/2015 a15-240 v15013 aThe Biological and Chemical Oceanography Data Management Office (BCO-DMO) and the Rolling Deck to Repository (R2R) program are two key data repositories for oceanographic research, supported by the U.S. National Science Foundation (NSF). R2R curates digital data and documentation generated by environmental sensor systems installed on vessels from the U.S. academic research fleet, with support from the NSF Oceanographic Technical Services and Arctic Research Logistics Programs. BCO-DMO human-curates and maintains data and metadata including biological, chemical, and physical measurements and results from projects funded by the NSF Biological Oceanography, Chemical Oceanography, and Antarctic Organisms & Ecosystems Programs. These two repositories have a strong connection, and document several thousand U.S. oceanographic research expeditions since the 1970’s. Recently, R2R and BCO-DMO have made their metadata collections available as Linked Data, accessible via public SPARQL endpoints. In this paper, we report on these datasets.1 aKrisnadhi, Adila1 aArko, Robert1 aCarbotte, Suzanne1 aChandler, Cynthia1 aCheatham, Michelle1 aHitzler, Pascal1 aHu, Yingjie1 aJanowicz, Krzysztof1 aJi, Peng1 aKarima, Nazifa1 aShepherd, Adam1 aWiebe, Peter1 ad'Amato, Claudia1 aLécué, Freddy1 aMutharaju, Raghava1 aNarock, Thomas1 aWirth, Fabian uhttps://daselab.cs.ksu.edu/publications/r2rbco-dmo-%E2%80%93-linked-oceanographic-datasets00452nas a2200133 4500008004100000245004600041210004600087260003900133100001800172700001800190700001300208700001500221856008200236 2015 eng d00aScalable Euclidean Embedding for Big Data0 aScalable Euclidean Embedding for Big Data aNew York City, NY bIEEE c07/20151 aAlavi, Zohreh1 aSharma, Sagar1 aZhou, Lu1 aChen, Keke uhttps://daselab.cs.ksu.edu/publications/scalable-euclidean-embedding-big-data00435nas a2200133 4500008004100000245004400041210004000085260001900125100001600144700002400160700002000184700002000204856007700224 2015 eng d00aThe Semantic Web Journal as Linked Data0 aSemantic Web Journal as Linked Data bSpringerc20151 aHu, Yingjie1 aJanowicz, Krzysztof1 aHitzler, Pascal1 aSengupta, Kunal uhttps://daselab.cs.ksu.edu/publications/semantic-web-journal-linked-data00453nas a2200121 4500008004100000245006600041210006100107260001200168490000600180100002000186700002400206856010100230 2015 eng d00aThe Semantic Web Journal Review Process: Transparent and Open0 aSemantic Web Journal Review Process Transparent and Open c06/20150 v31 aHitzler, Pascal1 aJanowicz, Krzysztof uhttps://daselab.cs.ksu.edu/publications/semantic-web-journal-review-process-transparent-and-open00416nas a2200145 4500008004100000245002700041210002700068300001000095490000700105100002400112700002300136700002000159700002400179856006700203 2015 eng d00aSemantics for Big Data0 aSemantics for Big Data a3–40 v361 avan Harmelen, Frank1 aHendler, James, A.1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttp://www.aaai.org/ojs/index.php/aimagazine/article/view/255901641nas a2200193 4500008004100000245009200041210006900133260002300202520093400225100002401159700001801183700002301201700001801224700001801242700001901260700002201279700002101301856012501322 2015 eng d00aSocial Signal Processing for Real-time Situational Understanding: a Vision and Approach0 aSocial Signal Processing for Realtime Situational Understanding aDallas, Texas, USA3 aThe US Army Research Laboratory (ARL) and the Air Force Research Laboratory (AFRL) have established a collaborative research enterprise referred to as the Situational Understanding Research Institute (SURI). The goal is to develop an information processing framework to help the military obtain real-time situational awareness of physical events by harnessing the combined power of multiple sensing sources to obtain insights about events and their evolution. It is envisioned that one could use such information to predict behaviors of groups, be they local transient groups (e.g., protests) or widespread, networked groups, and thus enable proactive prevention of nefarious activities. This paper presents a vision of how social media sources can be exploited in the above context to obtain insights about events, groups, and their evolution.
1 aJayarajah, Kasthuri1 aYao, Shuochao1 aMutharaju, Raghava1 aMisra, Archan1 aDe Mel, Geeth1 aSkipper, Julie1 aAbdelzaher, Tarek1 aKolodny, Michael uhttps://daselab.cs.ksu.edu/publications/social-signal-processing-real-time-situational-understanding-vision-and-approach01049nas a2200133 4500008004100000245006100041210006100102260003300163520055500196100002300751700002200774700002000796856009900816 2015 eng d00aTowards a Rule Based Distributed OWL Reasoning Framework0 aTowards a Rule Based Distributed OWL Reasoning Framework aBethlehem, PA, USAbSpringer3 aThe amount of data exposed in the form of RDF and OWL continues to increase exponentially. Some approaches have already been proposed for the scalable reasoning over several language profiles such as RDFS, OWL Horst, OWL 2 EL, OWL 2 RL etc. But all those approaches are limited to the particular ruleset that the reasoner supports. In this work, we propose the idea for a rule-based distributed reasoning framework that can support any given ruleset and highlight some of the challenges that needs to be solved in order to implement such a framework.1 aMutharaju, Raghava1 aMateti, Prabhaker1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/towards-rule-based-distributed-owl-reasoning-framework00459nas a2200121 4500008004100000245006500041210006500106260001300171300001200184100002000196700002000216856010100236 2015 eng d00aTowards Defeasible Mappings for Tractable Description Logics0 aTowards Defeasible Mappings for Tractable Description Logics bSpringer a237-2521 aSengupta, Kunal1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/towards-defeasible-mappings-tractable-description-logics01741nas a2200157 4500008004100000245004400041210004400085300001100129490000700140520127800147100002401425700002401449700002301473700002001496856006701516 2015 eng d00aWhy the Data Train Needs Semantic Rails0 aWhy the Data Train Needs Semantic Rails a5–140 v363 aWhile catchphrases such as big data, smart data, data intensive science, or smart dust highlight different aspects, they share a common theme: Namely, a shift towards a data-centric perspective in which the synthesis and analysis of data at an ever-increasing spatial, temporal, and thematic resolution promises new insights, while, at the same time, reducing the need for strong domain theories as starting points. In terms of the envisioned methodologies, those catchphrases tend to emphasize the role of predictive analytics, i.e., statistical techniques including data mining and machine learning, as well as supercomputing. Interestingly, however, while this perspective takes the availability of data as a given, it does not answer the question how one would discover the required data in today’s chaotic information universe, how one would understand which datasets can be meaningfully integrated, and how to communicate the results to humans and machines alike. The Semantic Web addresses these questions. In the following, we argue why the data train needs semantic rails. We point out that making sense of data and gaining new insights works best if inductive and deductive techniques go hand-in-hand instead of competing over the prerogative of interpretation.1 aJanowicz, Krzysztof1 avan Harmelen, Frank1 aHendler, James, A.1 aHitzler, Pascal uhttp://www.aaai.org/ojs/index.php/aimagazine/article/view/256001097nas a2200229 4500008004100000245008000041210006900121260002500190300001100215490000900226520039900235653002300634653001600657653000800673100001800681700002100699700002300720700002000743700001600763700002800779856006000807 2014 eng d00aAll But Not Nothing: Left-Hand Side Universals for Tractable {OWL} Profiles0 aAll But Not Nothing LeftHand Side Universals for Tractable OWL P bCEUR-WS.orgc10/2014 a97-1080 v12653 aWe show that occurrences of the universal quantifier in the left-hand side of general concept inclusions can be rewritten into EL++ axioms under certain circumstances. I.e., this intuitive modeling feature is available for OWL EL while retaining tractability. Furthermore, this rewriting makes it possible to reason over corresponding extensions of EL++ and Horn-SROIQ using standard reasoners.10adescription logics10aHorn Logics10aOWL1 aCarral, David1 aKrisnadhi, Adila1 aRudolph, Sebastian1 aHitzler, Pascal1 aKeet, Maria1 aTamma, Valentina, A. M. uhttp://ceur-ws.org/Vol-1265/owled2014_submission_13.pdf00691nas a2200169 4500008004100000245012700041210007100168300001400239490000800253100001800261700002100279700001900300700002200319700002800341700001900369856013300388 2014 eng d00aAnalytical modeling and simulation of I–V characteristics in carbon nanotube based gas sensors using ANN and SVR methods0 aAnalytical modeling and simulation of I–V characteristics in car a173–1800 v1371 aAkbari, Elnaz1 aBuntat, Zolkafle1 aEnzevaee, Aria1 aEbrahimi, Monireh1 aYazdavar, Amir, Hossein1 aYusof, Rubiyah uhttps://daselab.cs.ksu.edu/publications/analytical-modeling-and-simulation-i%E2%80%93v-characteristics-carbon-nanotube-based-gas00610nas a2200157 4500008004100000020002200041245016500063210006900228260001300297490000900310100001800319700002000337700002100357700002600378856004800404 2014 eng d a978-3-319-10553-600aArtificial Intelligence: Methodology, Systems, and Applications - 16th International Conference, AIMSA 2014, Varna, Bulgaria, September 11-13, 2014. Proceedings0 aArtificial Intelligence Methodology Systems and Applications 16t bSpringer0 v87221 aAgre, Gennady1 aHitzler, Pascal1 aKrisnadhi, Adila1 aKuznetsov, Sergei, O. uhttp://dx.doi.org/10.1007/978-3-319-10554-300485nas a2200145 4500008004100000245005000041210005000091490000600141100002000147700003000167700001600197700002000213700002000233856008600253 2014 eng d00aCombining Learning and Reasoning for Big Data0 aCombining Learning and Reasoning for Big Data0 v91 aHitzler, Pascal1 aGarcez, Artur, S. d'Avila1 aGori, Marco1 aHitzler, Pascal1 aLamb, Luís, C. uhttps://daselab.cs.ksu.edu/publications/combining-learning-and-reasoning-big-data01738nas a2200301 4500008004100000245007500041210006900116260008000185300001200265490000900277520074900286653001401035653000901049653002301058100002301081700002001104700001601124700002101140700002301161700001701184700002401201700002101225700002001246700002101266700002401287700002201311856010301333 2014 eng d00aConference v2.0: An uncertain version of the OAEI Conference benchmark0 aConference v20 An uncertain version of the OAEI Conference bench aRiva del Garda, ItalybLecture Notes in Computer Science, Springerc10/2014 a148-1630 v87973 aThe Ontology Alignment Evaluation Initiative is a set of benchmarks for evaluating the performance of ontology alignment systems. In this paper we re-examine the Conference track of the OAEI, with a focus on the degree of agreement between the reference alignments within this track and the opinion of experts. We propose a new version of this benchmark that more closely corresponds to expert opinion and confidence on the matches. The performance of top alignment systems is compared on both versions of the benchmark. Additionally, a general method for crowdsourcing the development of more benchmarks of this type using Amazon’s Mechanical Turk is introduced and shown to be scalable, cost-effective and to agree well with expert opinion.10abenchmark10aOAEI10aOntology Alignment1 aCheatham, Michelle1 aHitzler, Pascal1 aMika, Peter1 aTudorache, Tania1 aBernstein, Abraham1 aWelty, Chris1 aKnoblock, Craig, A.1 aVrandecic, Denny1 aGroth, Paul, T.1 aNoy, Natasha, F.1 aJanowicz, Krzysztof1 aGoble, Carole, A. uhttps://daselab.cs.ksu.edu/publications/conference-v20-uncertain-version-oaei-conference-benchmark00448nas a2200169 4500008004100000245002300041210002300064260001300087300001200100490000600112100002000118700002000138700002100158700001600179700002000195856006300215 2014 eng d00aDescription Logics0 aDescription Logics bElsevier a679-7100 v91 aKnorr, Matthias1 aHitzler, Pascal1 aGabbay, Dov., M.1 aWoods, John1 aSiekmann, Jörg uhttps://daselab.cs.ksu.edu/publications/description-logics00327nas a2200121 4500008004100000245002300041210002300064260001300087300001200100100002100112700002000133856005200153 2014 eng d00aDescription Logics0 aDescription Logics bSpringer a346-3511 aKrisnadhi, Adila1 aHitzler, Pascal uhttp://dx.doi.org/10.1007/978-1-4614-6170-8_10801144nas a2200181 4500008004100000245005900041210005900100260003900159300001400198490000900212520059400221100002300815700002200838700002000860700002000880700001800900856004400918 2014 eng d00aDeveloping a Distributed Reasoner for the Semantic Web0 aDeveloping a Distributed Reasoner for the Semantic Web aRiva del Garda, ItalybCEUR-WS.org a108–1120 v12683 aOWL 2 EL is one of the tractable profiles of the Web Ontology Language (OWL) which has been standardized by the W3C. OWL 2 EL provides suficient expressivity to model large biomedical ontologies as well streaming traffic data. Automated generation of ontologies from streaming data and text can lead to very large ontologies. There is a need to develop scalable reasoning approaches which scale with the size of the ontologies. We briefly describe our distributed reasoner, DistEL along with our experience and lessons learned during its development.
1 aMutharaju, Raghava1 aMateti, Prabhaker1 aHitzler, Pascal1 aVerborgh, Ruben1 aMannens, Erik uhttp://ceur-ws.org/Vol-1268/paper18.pdf01448nas a2200217 4500008004100000245005100041210005000092260004800142300001000190490000900200520077600209653002600985653001101011653001601022100002301038700002001061700002201081700002101103700002001124856008601144 2014 eng d00aDistributed OWL EL Reasoning: The Story So Far0 aDistributed OWL EL Reasoning The Story So Far aRiva del Garda, ItalybCEUR-WS.orgc10/2014 a61-760 v12613 aAutomated generation of axioms from streaming data, such as traffic and text, can result in very large ontologies that single machine reasoners cannot handle. Reasoning with large ontologies requires distributed solutions. Scalable reasoning techniques for RDFS, OWL Horst and OWL 2 RL now exist. For OWL 2 EL, several distributed reasoning approaches have been tried, but are all perceived to be inefficient. We analyze this perception. We analyze completion rule based distributed approaches, using different characteristics, such as dependency among the rules, implementation optimizations, how axioms and rules are distributed. We also present a distributed queue approach for the classification of ontologies in description logic EL+ (fragment of OWL 2 EL).
10aDistributed Reasoning10aOWL EL10aScalability1 aMutharaju, Raghava1 aHitzler, Pascal1 aMateti, Prabhaker1 aLiebig, Thorsten1 aFokoue, Achille uhttps://daselab.cs.ksu.edu/publications/distributed-owl-el-reasoning-story-so-far01017nas a2200205 4500008004100000245002500041210002400066300001400090520048400104653002300588653000800611653001400619653002400633100001800657700002000675700002700695700002000722700001800742856005100760 2014 eng d00aEL-ifying Ontologies0 aELifying Ontologies a464–4793 aThe OWL 2 profiles are fragments of the ontology language OWL 2 for which standard reasoning tasks are feasible in polynomial time. Many OWL ontologies, however, contain a typically small number of out-of-profile axioms, which may have little or no influence on reasoning outcomes. We investigate techniques for rewriting axioms into the EL and RL profiles of OWL 2. We have tested our techniques on both classification and data reasoning tasks with encouraging results.
10adescription logics10aOWL10aRewriting10aTractable Reasoning1 aCarral, David1 aFeier, Cristina1 aGrau, Bernardo, Cuenca1 aHitzler, Pascal1 aHorrocks, Ian uhttp://dx.doi.org/10.1007/978-3-319-08587-6_3601924nas a2200121 4500008004100000245008200041210006900123520142200192100002701614700002701641700002001668856011401688 2014 eng d00aEmotion recognition from speech based on relevant feature and majority voting0 aEmotion recognition from speech based on relevant feature and ma3 aThis paper proposes an approach to detect emotion from human speech employing majority voting technique over several machine learning techniques. The contribution of this work is in two folds: firstly it selects those features of speech which is most promising for classification and secondly it uses the majority voting technique that selects the exact class of emotion. Here, majority voting technique has been applied over Neural Network (NN), Decision Tree (DT), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Input vector of NN, DT, SVM and KNN consists of various acoustic and prosodic features like Pitch, Mel-Frequency Cepstral coefficients etc. From speech signal many feature have been extracted and only promising features have been selected. To consider a feature as promising, Fast Correlation based feature selection (FCBF) and Fisher score algorithms have been used and only those features are selected which are highly ranked by both of them. The proposed approach has been tested on Berlin dataset of emotional speech [3] and Electromagnetic Articulography (EMA) dataset [4]. The experimental result shows that majority voting technique attains better accuracy over individual machine learning techniques. The employment of the proposed approach can effectively recognize the emotion of human beings in case of social robot, intelligent chat client, call-center of a company etc.
1 aSarker, Md Kamruzzaman1 aRokibul, Alam, Kazi Md1 aArifuzzaman, Md uhttps://daselab.cs.ksu.edu/publications/emotion-recognition-speech-based-relevant-feature-and-majority-voting00703nas a2200241 4500008004100000245004100041210004100082260002000123100002200143700001900165700001900184700001900203700001700222700001300239700001500252700001800267700002000285700002100305700001900326700001800345700001700363856008100380 2014 eng d00aEnhancing Ocean Research Data Access0 aEnhancing Ocean Research Data Access aVienna, Austria1 aChandler, Cynthia1 aGroman, Robert1 aShepherd, Adam1 aAllison, Molly1 aArko, Robert1 aChen, Yu1 aFox, Peter1 aGlover, David1 aHitzler, Pascal1 aLeadbetter, Adam1 aNarock, Thomas1 aWest, Patrick1 aWiebe, Peter uhttps://daselab.cs.ksu.edu/publications/enhancing-ocean-research-data-access01203nas a2200169 4500008004100000245004500041210004500086300001400131490000600145520074000151100002400891700002000915700002000935700001600955700002200971856004000993 2014 eng d00aFive stars of Linked Data vocabulary use0 aFive stars of Linked Data vocabulary use a173–1760 v53 aIn 2010 Tim Berners-Lee introduced a 5 star rating to his Linked Data design issues page to encourage data publishers along the road to good Linked Data. What makes the star rating so effective is its simplicity, clarity, and a pinch of psychology – is your data 5 star? While there is an abundance of 5 star Linked Data available today, finding, querying, and integrating/interlinking these data is, to say the least, difficult. While the literature has largely focused on describing datasets, e.g., by adding provenance information, or interlinking them, e.g., by co-reference resolution tools, we would like to take Berners-Lee’s original proposal to the next level by introducing a 5 star rating for Linked Data vocabulary use.1 aJanowicz, Krzysztof1 aHitzler, Pascal1 aAdams, Benjamin1 aKolas, Dave1 aVardeman, Charles uhttp://dx.doi.org/10.3233/SW-14013500857nas a2200253 4500008004100000245009600041210006900137260001300206300001400219490000900233100002200242700002000264700001700284700002000301700002000321700002000341700002400361700002100385700001900406700002100425700001900446700001700465856012100482 2014 eng d00aHow to Best Find a Partner? An Evaluation of Editing Approaches to Construct R2RML Mappings0 aHow to Best Find a Partner An Evaluation of Editing Approaches t bSpringer a675–6900 v84651 aPinkel, Christoph1 aBinnig, Carsten1 aHaase, Peter1 aMartin, Clemens1 aSengupta, Kunal1 aTrame, Johannes1 aPresutti, Valentina1 ad'Amato, Claudia1 aGandon, Fabien1 ad'Aquin, Mathieu1 aStaab, Steffen1 aTordai, Anna uhttps://daselab.cs.ksu.edu/publications/how-best-find-partner-evaluation-editing-approaches-construct-r2rml-mappings00496nas a2200181 4500008004100000245003200041210003200073260001300105300001200118490000600130100002000136700001800156700001900174700002100193700001600214700002000230856006400250 2014 eng d00aLogics for the Semantic Web0 aLogics for the Semantic Web bElsevier a679-7100 v91 aHitzler, Pascal1 aLehmann, Jens1 aPolleres, Axel1 aGabbay, Dov., M.1 aWoods, John1 aSiekmann, Jörg uhttps://daselab.cs.ksu.edu/publications/logics-semantic-web00468nas a2200145 4500008004100000245006800041210006500109300001200174490000600186100003000192700001600222700002000238700002000258856004400278 2014 eng d00aNeural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381)0 aNeuralSymbolic Learning and Reasoning Dagstuhl Seminar 14381 a50–840 v41 aGarcez, Artur, S. d'Avila1 aGori, Marco1 aHitzler, Pascal1 aLamb, Luís, C. uhttp://dx.doi.org/10.4230/DagRep.4.9.5000576nas a2200205 4500008004100000245002600041210002200067100001900089700001700108700002200125700002200147700002300169700001900192700002000211700002100231700001800252700001900270700001700289856006400306 2014 eng d00aThe OceanLink Project0 aOceanLink Project1 aNarock, Thomas1 aArko, Robert1 aCarbotte, Suzanne1 aChandler, Cynthia1 aCheatham, Michelle1 aFinin, Timothy1 aHitzler, Pascal1 aKrisnadhi, Adila1 aRaymond, Lisa1 aShepherd, Adam1 aWiebe, Peter uhttps://daselab.cs.ksu.edu/publications/oceanlink-project-001693nas a2200385 4500008004100000020002200041245002800063210002200091260002000113300001000133520067700143100001900820700001700839700002200856700002100878700002000899700002300919700001900942700002200961700001800983700001701001700001901018700001501037700001401052700001601066700001401082700002301096700002001119700001801139700002001157700001401177700002201191700002201213856007201235 2014 eng d a978-1-4799-5665-400aThe {OceanLink} project0 aOceanLink project b{IEEE}c10/2014 a14-213 aToday's scientific investigations are producing large numbers of scholarly products. These products continue to increase in diversity and complexity as researchers recognize that scholarly achievements are not only published articles but also datasets, software, and associated supporting materials. OceanLink is an online platform that addresses scholarly discovery and collaboration in the ocean sciences. The OceanLink project leverages Semantic Web technologies, web mining, and crowdsourcing to identify links between data centers, digital repositories, and professional societies to enhance discovery, enable collaboration, and begin to assess research contribution.1 aNarock, Thomas1 aArko, Robert1 aCarbotte, Suzanne1 aKrisnadhi, Adila1 aHitzler, Pascal1 aCheatham, Michelle1 aShepherd, Adam1 aChandler, Cynthia1 aRaymond, Lisa1 aWiebe, Peter1 aFinin, Timothy1 aLin, Jimmy1 aPei, Jian1 aHu, Xiaohua1 aChang, Wo1 aNambiar, Raghunath1 aAggarwal, Charu1 aCercone, Nick1 aHonavar, Vasant1 aHuan, Jun1 aMobasher, Bamshad1 aPyne, Saumyadipta uhttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=697386100998nas a2200193 4500008004100000245005400041210005100095300001200146520046200158653001300620653002800633653000800661100001800669700001600687700001800703700001600721700002400737856004300761 2014 eng d00aAn Ontology Design Pattern for Activity Reasoning0 aOntology Design Pattern for Activity Reasoning a78–813 aActivity is an important concept in many fields, and a number of activity-related ontologies have been developed. While suitable for their designated use cases, these ontologies cannot be easily generalized to other applications. This paper aims at providing a generic ontology design pattern to model the common core of activities in different domains. Such a pattern can be used as a building block to construct more specific activity ontologies.
10aActivity10aOntology Design Pattern10aOWL1 aAbdalla, Amin1 aHu, Yingjie1 aCarral, David1 aLi, Naicong1 aJanowicz, Krzysztof uhttp://ceur-ws.org/Vol-1302/paper8.pdf01137nas a2200229 4500008004100000245007100041210006600112260002500178300001000203490000900213520046800222100001600690700002100706700001500727700002400742700002000766700002000786700001800806700002400824700002700848856003200875 2014 eng d00aAn Ontology Design Pattern for Cooking Recipes - Classroom Created0 aOntology Design Pattern for Cooking Recipes Classroom Created bCEUR-WS.orgc10/2014 a49-600 v13023 aWe present a description and result of an ontology modeling process taken to the classroom. The application domain considered was cooking recipes. The modeling goal was to bridge heterogeneity across representational choices by developing a content ontology design pattern which is general enough to allow for the integration of information from different web sites. We will discuss the pattern developed, and report on corresponding insights and lessons learned.1 aSam, Monica1 aKrisnadhi, Adila1 aWang, Cong1 aGallagher, John, C.1 aHitzler, Pascal1 ade Boer, Victor1 aGangemi, Aldo1 aJanowicz, Krzysztof1 aLawrynowicz, Agnieszka uhttp://ceur-ws.org/Vol-130201183nas a2200289 4500008004100000245005900041210005600100260002500156300001000181490000900191520034600200100002200546700002100568700002300589700002400612700002000636700002000656700002200676700003200698700002100730700002100751700002000772700001800792700002400810700002700834856003200861 2014 eng d00aAn Ontology Design Pattern for Material Transformation0 aOntology Design Pattern for Material Transformation bCEUR-WS.orgc10/2014 a73-770 v13023 aIn this work we discuss an ontology design pattern for material transformations. It models the relation between products, resources, and catalysts in the transformation process. Our axiomatization goes beyond a mere surface semantics. While we focus on the construction domain, the pattern can also be applied to chemistry and other domains.1 aVardeman, Charles1 aKrisnadhi, Adila1 aCheatham, Michelle1 aJanowicz, Krzysztof1 aFerguson, Holly1 aHitzler, Pascal1 aBuccellato, Aimee1 aThirunarayan, Krishnaprasad1 aBerg-Cross, Gary1 aHahmann, Torsten1 ade Boer, Victor1 aGangemi, Aldo1 aJanowicz, Krzysztof1 aLawrynowicz, Agnieszka uhttp://ceur-ws.org/Vol-130200627nas a2200169 4500008004100000245007600041210006900117260003300186300001100219490000900230100002000239700002400259700002200283700002100305700001900326856011200345 2014 eng d00aOntology Design Patterns for Large-Scale Data Interchange and Discovery0 aOntology Design Patterns for LargeScale Data Interchange and Dis aLinköping, SwedenbSpringer aXIX-XX0 v88761 aHitzler, Pascal1 aJanowicz, Krzysztof1 aSchlobach, Stefan1 aLambrix, Patrick1 aHyvönen, Eero uhttps://daselab.cs.ksu.edu/publications/ontology-design-patterns-large-scale-data-interchange-and-discovery00894nas a2200289 4500008004100000245006200041210006200103490000600165100002000171700002100191700001700212700002200229700002200251700002300273700001900296700002400315700001900339700001800358700001900376700001700395700001800412700002300430700001700453700002000470700001600490856009800506 2014 eng d00aOntology Design Patterns for Ocean Science Data Discovery0 aOntology Design Patterns for Ocean Science Data Discovery0 v31 aHitzler, Pascal1 aKrisnadhi, Adila1 aArko, Robert1 aCarbotte, Suzanne1 aChandler, Cynthia1 aCheatham, Michelle1 aFinin, Timothy1 aJanowicz, Krzysztof1 aNarock, Thomas1 aRaymond, Lisa1 aShepherd, Adam1 aWiebe, Peter1 aGangemi, Aldo1 aHafner, Verena, V.1 aKuhn, Werner1 aScheider, Simon1 aSteels, Luc uhttps://daselab.cs.ksu.edu/publications/ontology-design-patterns-ocean-science-data-discovery02084nas a2200229 4500008004100000245011700041210006900158520125400227100002101481700001701502700002201519700002201541700002301563700001901586700002001605700002401625700001901649700001801668700001901686700001701705856013201722 2014 eng d00aAn Ontology Pattern for Oceanograhic Cruises: Towards an Oceanographer's Dream of Integrated Knowledge Discovery0 aOntology Pattern for Oceanograhic Cruises Towards an Oceanograph3 aEarthCube is a major effort of the National Science Foundation to establish a next-generation knowledge architecture for the broader geosciences. Data storage, retrieval, access, and reuse are central parts of this new effort. Currently, EarthCube is organized around several building blocks and research coordination networks. OceanLink is a semanticsenabled building block that aims at improving data retrieval and reuse via ontologies, Semantic Web technologies, and Linked Data for the ocean sciences. Cruises, in the sense of research expeditions, are central events for ocean scientists. Consequently, information about these cruises and the involved vessels has to be shared and made retrievable. For example, the ability to find cruises in the vicinity of physiographic features of interest, e.g., a hydrothermal vent field or a fracture zone, is of primary interest for oceanographers. In this paper, we use a design pattern-centric strategy to engineer ontologies for OceanLink. We provide a formal axiomatization of the introduced patterns and ontologies using the Web Ontology Language, explain design choices, discuss the re-usability of our models, and provide lessons learned for the future geo-ontologies.
1 aKrisnadhi, Adila1 aArko, Robert1 aCarbotte, Suzanne1 aChandler, Cynthia1 aCheatham, Michelle1 aFinin, Timothy1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aNarock, Thomas1 aRaymond, Lisa1 aShepherd, Adam1 aWiebe, Peter uhttps://daselab.cs.ksu.edu/publications/ontology-pattern-oceanograhic-cruises-towards-oceanographers-dream-integrated-knowledge00372nas a2200109 4500008004100000245004100041210003700082260002600119100002300145700002000168856007400188 2014 eng d00aThe Properties of Property Alignment0 aProperties of Property Alignment aRiva del Garda, Italy1 aCheatham, Michelle1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/properties-property-alignment00830nas a2200277 4500008004100000245004600041210004600087100001900133700001700152700002200169700002200191700002300213700001800236700001900254700002000273700002400293700001900317700002100336700002100357700001900378700001800397700002200415700001900437700001700456856007900473 2014 eng d00aProvenance Usage in the OceanLink Project0 aProvenance Usage in the OceanLink Project1 aNarock, Thomas1 aArko, Robert1 aCarbotte, Suzanne1 aChandler, Cynthia1 aCheatham, Michelle1 aFils, Douglas1 aFinin, Timothy1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aJones, Matthew1 aKrisnadhi, Adila1 aLehnert, Kerstin1 aMickle, Audrey1 aRaymond, Lisa1 aSchildhauer, Mark1 aShepherd, Adam1 aWiebe, Peter uhttps://daselab.cs.ksu.edu/publications/provenance-usage-oceanlink-project01073nas a2200193 4500008004100000245005900041210005900100300001400159520049700173653002300670653000800693653002400701100001800725700002000743700002700763700002000790700001800810856005100828 2014 eng d00aPushing the Boundaries of Tractable Ontology Reasoning0 aPushing the Boundaries of Tractable Ontology Reasoning a148–1633 aWe identify a class of Horn ontologies for which standard reasoning tasks such as instance checking and classification are tractable. The class is general enough to include the OWL 2 EL, QL, and RL profiles. Verifying whether a Horn ontology belongs to the class can be done in polynomial time. We show empirically that the class includes many real-world ontologies that are not included in any OWL 2 profile, and thus that polynomial time reasoning is possible for these ontologies.
10adescription logics10aOWL10aTractable Reasoning1 aCarral, David1 aFeier, Cristina1 aGrau, Bernardo, Cuenca1 aHitzler, Pascal1 aHorrocks, Ian uhttp://dx.doi.org/10.1007/978-3-319-11915-1_1000492nas a2200133 4500008004100000245006800041210006600109260001900175100001800194700001300212700001800225700001500243856010000258 2014 eng d00aRASP-QS: Efficient and Confidential Query Services in the Cloud0 aRASPQS Efficient and Confidential Query Services in the Cloud aVLDB Endowment1 aAlavi, Zohreh1 aZhou, Lu1 aPowers, James1 aChen, Keke uhttps://daselab.cs.ksu.edu/publications/rasp-qs-efficient-and-confidential-query-services-cloud00282nas a2200109 4500008004100000245001400041210001400055300001600069100001500085700002000100856005200120 2014 eng d00aReasoning0 aReasoning a1499–15011 aWang, Cong1 aHitzler, Pascal uhttp://dx.doi.org/10.1007/978-1-4614-6170-8_11500786nas a2200265 4500008004100000245005100041210005100092300001200143490000700155100001700162700001700179700001300196700003200209700002000241700001700261700001600278700002300294700002500317700002400342700002300366700002000389700002400409700002000433856006700453 2014 eng d00aReports on the 2013 AAAI Fall Symposium Series0 aReports on the 2013 AAAI Fall Symposium Series a69–740 v351 aBurns, Gully1 aGil, Yolanda1 aLiu, Yan1 aVillanueva-Rosales, Natalia1 aRisi, Sebastian1 aLehman, Joel1 aClune, Jeff1 aLebiere, Christian1 aRosenbloom, Paul, S.1 avan Harmelen, Frank1 aHendler, James, A.1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aSwarup, Samarth uhttp://www.aaai.org/ojs/index.php/aimagazine/article/view/253801531nas a2200265 4500008004100000245008400041210006900125260007900194300000900273490000900282520061700291653001800908653001300926653002300939653002300962100002000985700002001005700002401025700001701049700001801066700001801084700001701102700001901119856012701138 2014 eng d00aRevisiting default description logics – and their role in aligning ontologies0 aRevisiting default description logics and their role in aligning aChiang Mai, ThailandbLecture Notes in Computer Science, Springerc11/2014 a3-180 v89433 aWe present a new approach to extend the Web Ontology Language (OWL) with the capabilities to reason with defaults. This work improves upon the previously established results on integrating defaults with description logics (DLs), which were shown to be decidable only when the application of defaults is restricted to named individuals in the knowledge base. We demonstrate that the application of defaults (integrated with DLs) does not have to be restricted to named individuals to retain decidability and elaborate on the application of defaults in the context of ontology alignment and ontology-based systems.10adefault logic10adefaults10adescription logics10aOntology Alignment1 aSengupta, Kunal1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aSupnithi, T.1 aYamaguchi, T.1 aPan, Jeff, Z.1 aWuwongse, V.1 aBuranarach, M. uhttps://daselab.cs.ksu.edu/publications/revisiting-default-description-logics-%E2%80%93-and-their-role-aligning-ontologies03809nas a2200229 4500008004100000245007100041210006900112260001200181300000900193490000700202520308900209100001903298700002203317700001703339700001803356700002103374700002003395700001903415700001903434700001903453856010703472 2014 eng d00aSemantic Entity Pairing for Improved Data Validation and Discovery0 aSemantic Entity Pairing for Improved Data Validation and Discove c05/2014 a24760 v163 aOne of the central incentives for linked data implementations is the opportunity to leverage the rich logic inherent in structured data. The logic embedded in semantic models can strengthen capabilities for data discovery and data validation when pairing entities from distinct, contextually-related datasets. The creation of links between the two datasets broadens data discovery by using the semantic logic to help machines compare similar entities and properties that exist on different levels of granularity. This semantic capability enables appropriate entity pairing without making inaccurate assertions as to the nature of the relationship. Entity pairing also provides a context to accurately validate the correctness of an entity's property values - an exercise highly valued by data management practices who seek to ensure the quality and correctness of their data. The Biological and Chemical Oceanography Data Management Office (BCO-DMO) semantically models metadata surrounding oceanographic researchcruises, but other sources outside of BCO-DMO exist that also model metadata about these same cruises. For BCO-DMO, the process of successfully pairing its entities to these sources begins by selecting sources that are decidedly trustworthy and authoritative for the modeled concepts. In this case, the Rolling Deck to Repository (R2R) program has a well-respected reputation among the oceanographic research community, presents a data context that is uniquely different and valuable, and semantically models its cruise metadata. Where BCO-DMO exposes the processed, analyzed data products generated by researchers, R2R exposes the raw shipboard data that was collected on the same research cruises. Interlinking these cruise entities expands data discovery capabilities but also allows for validating the contextual correctness of both BCO-DMO's and R2R's cruise metadata. Assessing the potential for a link between two datasets for a similar entity consists of aligning like properties and deciding on the appropriate semantic markup to describe the link. This highlights the desire for research organizations like BCO-DMO and R2R to ensure the complete accuracy of their exposed metadata, as it directly reflects on their reputations as successful and trustworthy source of research data. Therefore, data validation reaches beyond simple syntax of property values into contextual correctness. As a human process, this is a time-intensive task that does not scale well for finite human and funding resources. Therefore, to assess contextual correctness across datasets at different levels of granularity, BCO-DMO is developing a system that employs semantic technologies to aid the human process by organizing potential links and calculating a confidence coefficient as to the correctness of the potential pairing based on the distance between certain entity property values. The system allows humans to quickly scan potential links and their confidence coefficients for asserting persistence and correcting and investigating misaligned entity property values.
1 aShepherd, Adam1 aChandler, Cynthia1 aArko, Robert1 aChen, Yanning1 aKrisnadhi, Adila1 aHitzler, Pascal1 aNarock, Thomas1 aGroman, Robert1 aRauch, Shannon uhttps://daselab.cs.ksu.edu/publications/semantic-entity-pairing-improved-data-validation-and-discovery00476nas a2200181 4500008004100000245001700041210001700058250000600075260002500081300001700106490000600123100002000129700002400149700001800173700002200191700002400213856005700237 2014 eng d00aSemantic Web0 aSemantic Web a3 bChapman and Hall/CRC a50-1 - 50-130 vI1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aTucker, Allen1 aGonzalez, Teofilo1 aDiaz-Herrera, Jorge uhttps://daselab.cs.ksu.edu/publications/semantic-web01044nas a2200361 4500008004100000245008000041210006900121300001400190490000600204100001500210700002400225700001900249700001800268700001800286700002100304700002000325700002400345700002000369700001700389700002100406700002300427700002300450700001700473700002000490700001700510700001800527700002100545700002000566700002300586700001800609700001500627856004000642 2014 eng d00aSemantic Web and Big Data meets Applied Ontology - The Ontology Summit 20140 aSemantic Web and Big Data meets Applied Ontology The Ontology Su a155–1700 v91 aObrst, Leo1 aGrüninger, Michael1 aBaclawski, Ken1 aBennett, Mike1 aBrickley, Dan1 aBerg-Cross, Gary1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aKapp, Christine1 aKutz, Oliver1 aLange, Christoph1 aLevenchuk, Anatoly1 aQuattri, Francesca1 aRector, Alan1 aSchneider, Todd1 aSpero, Simon1 aThessen, Anne1 aVegetti, Marcela1 aVizedom, Amanda1 aWesterinen, Andrea1 aWest, Matthew1 aYim, Peter uhttp://dx.doi.org/10.3233/AO-14013500719nas a2200181 4500008004100000245015100041210006900192300001600261490000600277100001400283700002200297700002800319700001600347700001400363700002000377700001100397856012900408 2014 eng d00aTransmission of data with orthogonal frequency division multiplexing technique for communication networks using GHz frequency band soliton carrier0 aTransmission of data with orthogonal frequency division multiple a1364–13730 v81 aAmiri, IS1 aEbrahimi, Monireh1 aYazdavar, Amir, Hossein1 aGhorbani, S1 aAlavi, SE1 aIdrus, Sevia, M1 aAli, J uhttps://daselab.cs.ksu.edu/publications/transmission-data-orthogonal-frequency-division-multiplexing-technique-communication00921nas a2200265 4500008004100000245009400041210006900135100001800204700001900222700001700241700002200258700002200280700002300302700001800325700002000343700002400363700001900387700002100406700002100427700001900448700001900467700002200486700001700508856013000525 2014 eng d00aUsing Linked Open Data and Semantic Integration to Search Across Geoscience Repositories.0 aUsing Linked Open Data and Semantic Integration to Search Across1 aRaymond, Lisa1 aShepherd, Adam1 aArko, Robert1 aCarbotte, Suzanne1 aChandler, Cynthia1 aCheatham, Michelle1 aFils, Douglas1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aJones, Matthew1 aKrisnadhi, Adila1 aLehnert, Kerstin1 aMickle, Audrey1 aNarock, Thomas1 aSchildhauer, Mark1 aWiebe, Peter uhttps://daselab.cs.ksu.edu/publications/using-linked-open-data-and-semantic-integration-search-across-geoscience-repositories00395nas a2200133 4500008004100000245003200041210003000073300001600103100002000119700002000139700001700159700001500176856007000191 2014 eng d00aWeb Ontology Language (OWL)0 aWeb Ontology Language OWL a2374–23781 aSengupta, Kunal1 aHitzler, Pascal1 aAlhajj, Reda1 aRokna, Jon uhttps://daselab.cs.ksu.edu/publications/web-ontology-language-owl01419nas a2200205 4500008004100000245008100041210006900122300001400191520078200205653002300987653000801010653002401018100001801042700002001060700002301080700002701103700002001130700001801150856004501168 2014 eng d00aIs Your Ontology as Hard as You Think? Rewriting Ontologies into Simpler DLs0 aYour Ontology as Hard as You Think Rewriting Ontologies into Sim a128–1403 aWe investigate cases where an ontology expressed in a seemingly hard DL can be polynomially reduced to one in a simpler logic, while preserving reasoning outcomes for classification and fact entailment. Our transformations target the elimination of inverse roles, universal and existential restrictions, and in the best case allow us to rewrite the given ontology into one of the OWL 2 profiles. Even if an ontology cannot be fully rewritten into a profile, in many cases our transformations allow us to exploit further optimisation techniques. Moreover, the elimination of some out-of-profile axioms can improve the performance of modular reasoners, such as MORe. We have tested our techniques on both classification and data reasoning tasks with encouraging results.
10adescription logics10aOWL10aTractable Reasoning1 aCarral, David1 aFeier, Cristina1 aRomero, Ana, Armas1 aGrau, Bernardo, Cuenca1 aHitzler, Pascal1 aHorrocks, Ian uhttp://ceur-ws.org/Vol-1193/paper_75.pdf01378nas a2200193 4500008004100000245005700041210005700098260002100155300001400176520079900190653001900989653002601008653002701034100002301061700002001084700001601104700001801120856004601138 2013 eng d00aAutomatic Domain Identification for Linked Open Data0 aAutomatic Domain Identification for Linked Open Data aAtlanta, GA, USA a205–2123 aLinked Open Data (LOD) has emerged as one of the largest collections of interlinked structured datasets on the Web. Although the adoption of such datasets for applications is increasing, identifying relevant datasets for a specific task or topic is still challenging. As an initial step to make such identification easier, we provide an approach to automatically identify the topic domains of given datasets. Our method utilizes existing knowledge sources, more specifically Freebase, and we present an evaluation which validates the topic domains we can identify with our system. Furthermore, we evaluate the effectiveness of identified topic domains for the purpose of finding relevant datasets, thus showing that our approach improves reusability of LOD datasets.
10adataset search10aDomain Identification10aLinked Open Data Cloud1 aLalithsena, Sarasi1 aHitzler, Pascal1 aSheth, Amit1 aJain, Prateek uhttp://dx.doi.org/10.1109/WI-IAT.2013.20600422nas a2200133 4500008004100000245003700041210003700078260001800115100002000133700001700153700001700170700002400187856007700211 2013 eng d00aBridging KR and Machine Learning0 aBridging KR and Machine Learning aArlington, VA1 aHitzler, Pascal1 aGetoor, Lise1 aNoy, Natasha1 aMcGuinness, Deborah uhttps://daselab.cs.ksu.edu/publications/bridging-kr-and-machine-learning00448nas a2200121 4500008004100000245005600041210005400097260001800151100002000169700001700189700002400206856009600230 2013 eng d00aBridging open-world knowledge and closed-world data0 aBridging openworld knowledge and closedworld data aArlington, VA1 aHitzler, Pascal1 aNoy, Natasha1 aMcGuinness, Deborah uhttps://daselab.cs.ksu.edu/publications/bridging-open-world-knowledge-and-closed-world-data01631nas a2200181 4500008004100000245004400041210004400085300000600129490000700135520112800142653002901270653002301299653001501322100002201337700002301359700002001382856004701402 2013 eng d00aComplexities of Horn Description Logics0 aComplexities of Horn Description Logics a20 v143 aDescription Logics (DLs) have become a prominent paradigm for representing knowledge bases in a variety of application areas. Central to leveraging them for corresponding systems is the provision of a favourable balance between expressivity of the knowledge representation formalism on the one hand, and runtime performance of reasoning algorithms on the other. Due to this, Horn description logics (Horn DLs) have attracted attention since their (worst-case) data complexities are in general lower than their overall (i.e. combined) complexities, which makes them attractive for reasoning with large sets of instance data (ABoxes). However, the natural question whether Horn DLs also provide advantages for schema (TBox) reasoning has hardly been addressed so far. In this paper, we therefore provide a thorough and comprehensive analysis of the combined complexities of Horn DLs. While the combined complexity for many Horn DLs studied herein turns out to be the same as for their non-Horn counterparts, we identify subboolean DLs where Hornness simplifies reasoning. We also provide convenient normal forms for Horn DLs.10acomputational complexity10adescription logics10aHorn logic1 aKrötzsch, Markus1 aRudolph, Sebastian1 aHitzler, Pascal uhttp://doi.acm.org/10.1145/2422085.242208700590nas a2200157 4500008004100000245006800041210006800109260003300177100001600210700002000226700002400246700001700270700002000287700002400307856010100331 2013 eng d00aCrowdsourcing Semantics for Big Data in Geoscience Applications0 aCrowdsourcing Semantics for Big Data in Geoscience Applications aArlington, Virginiac11/20131 aNarock, Tom1 aHitzler, Pascal1 avan Harmelen, Frank1 aHendler, Jim1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttps://daselab.cs.ksu.edu/publications/crowdsourcing-semantics-big-data-geoscience-applications01783nas a2200253 4500008003900000245005000039210004800089260004400137300001000181490000900191520104900200653001901249653001101268653002601279653000801305653000801313653001601321100002301337700002001360700002201380700002101402700002001423856008601443 2013 d00aDistEL: A Distributed EL+ Ontology Classifier0 aDistEL A Distributed EL Ontology Classifier aSydney, AustraliabCEUR-WS.orgc10/2013 a17-320 v10463 aOWL 2 EL ontologies are used to model and reason over data from diverse domains such as biomedicine, geography and road traffic. Data in these domains is increasing at a rate quicker than the increase in main memory and computation power of a single machine. Recent efforts in OWL reasoning algorithms lead to the decrease in classification time from several hours to a few seconds even for large ontologies like SNOMED CT. This is especially true for ontologies in the description logic EL+ (a fragment of the OWL 2 EL profile). Reasoners such as Pellet, Hermit, ELK etc. make an assumption that the ontology would fit in the main memory, which is unreasonable given projected increase in data volumes. Increase in the data volume also necessitates an increase in the computation power. This lead us to the use of a distributed system, so that memory and computation requirements can be spread across machines. We present a distributed system for the classification of EL+ ontologies along with some results on its scalability and performance.10aClassification10aDistEL10aDistributed Reasoning10aEL+10aOWL10aScalability1 aMutharaju, Raghava1 aHitzler, Pascal1 aMateti, Prabhaker1 aLiebig, Thorsten1 aFokoue, Achille uhttps://daselab.cs.ksu.edu/publications/distel-distributed-el-ontology-classifier01528nas a2200241 4500008004100000245006600041210006300107260003500170300001400205490000900219520081200228653001201040653002501052653002601077653001101103100002301114700001701137700002101154700002001175700001901195700001701214856005501231 2013 eng d00aD-SPARQ: Distributed, Scalable and Efficient RDF Query Engine0 aDSPARQ Distributed Scalable and Efficient RDF Query Engine aSydney, AustraliabCEUR-WS.org a261–2640 v10353 aWe present D-SPARQ, a distributed RDF query engine that combines the MapReduce processing framework with a NoSQL distributed data store, MongoDB. The performance of processing SPARQL queries mainly depends on the efficiency of handling the join operations between the RDF triple patterns. Our system features two unique characteristics that enable efficiently tackling this challenge: 1) Identifying specific patterns of the input queries that enable improving the performance by running different parts of the query in a parallel mode. 2) Using the triple selectivity information for reordering the individual triples of the input query within the identified query patterns. The preliminary results demonstrate the scalability and efficiency of our distributed RDF query engine.
10aD-SPARQ10aDistributed Querying10aScalable RDF querying10aSPARQL1 aMutharaju, Raghava1 aSakr, Sherif1 aSala, Alessandra1 aHitzler, Pascal1 aBlomqvist, Eva1 aGroza, Tudor uhttp://ceur-ws.org/Vol-1035/iswc2013_poster_21.pdf00527nas a2200181 4500008004100000245003700041210003700078260001600115300001400131490000900145100002000154700001700174700002100191700002000212700001900232700001700251856007700268 2013 eng d00aEditing R2RML Mappings Made Easy0 aEditing R2RML Mappings Made Easy bCEUR-WS.org a101–1040 v10351 aSengupta, Kunal1 aHaase, Peter1 aSchmidt, Michael1 aHitzler, Pascal1 aBlomqvist, Eva1 aGroza, Tudor uhttps://daselab.cs.ksu.edu/publications/editing-r2rml-mappings-made-easy01947nas a2200241 4500008004100000245006000041210005700101300001400158520126400172653002801436653000801464653001501472100001601487700002401503700001801527700002001545700001701565700002101582700002001603700001501623700001601638856005101654 2013 eng d00aA Geo-ontology Design Pattern for Semantic Trajectories0 aGeoontology Design Pattern for Semantic Trajectories a438–4563 aTrajectory data have been used in a variety of studies, including human behavior analysis, transportation management, and wildlife tracking. While each study area introduces a different perspective, they share the need to integrate positioning data with domain-specific information. Semantic annotations are necessary to improve discovery, reuse, and integration of trajectory data from different sources. Consequently, it would be beneficial if the common structure encountered in trajectory data could be annotated based on a shared vocabulary, abstracting from domain-specific aspects. Ontology design patterns are an increasingly popular approach to define such flexible and self-contained building blocks of annotations. They appear more suitable for the annotation of interdisciplinary, multi-thematic, and multi-perspective data than the use of foundational and domain ontologies alone. In this paper, we introduce such an ontology design pattern for semantic trajectories. It was developed as a community effort across multiple disciplines and in a data-driven fashion. We discuss the formalization of the pattern using the Web Ontology Language (OWL) and apply the pattern to two different scenarios, personal travel and wildlife monitoring.
10aOntology Design Pattern10aOWL10aTrajectory1 aHu, Yingjie1 aJanowicz, Krzysztof1 aCarral, David1 aScheider, Simon1 aKuhn, Werner1 aBerg-Cross, Gary1 aHitzler, Pascal1 aDean, Mike1 aKolas, Dave uhttp://dx.doi.org/10.1007/978-3-319-01790-7_2400416nas a2200121 4500008004100000245004800041210004700089260001800136100002000154700001700174700002400191856007900215 2013 eng d00aGrand Challenge: From Big Data to Knowledge0 aGrand Challenge From Big Data to Knowledge aArlington, VA1 aHitzler, Pascal1 aNoy, Natasha1 aMcGuinness, Deborah uhttps://daselab.cs.ksu.edu/publications/grand-challenge-big-data-knowledge00358nas a2200097 4500008004100000245005000041210004900091260001800140100002000158856008200178 2013 eng d00aKnowledge Representation in the Big Data Age.0 aKnowledge Representation in the Big Data Age aArlington, VA1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/knowledge-representation-big-data-age00636nas a2200157 4500008004100000245009200041210006900133260002700202100001900229700002100248700002000269700001700289700002200306700002200328856012800350 2013 eng d00aLeveraging Crowdsourcing and Linked Open Data for Geoscience Data Sharing and Discovery0 aLeveraging Crowdsourcing and Linked Open Data for Geoscience Dat aSan Francisco, CA, USA1 aNarock, Thomas1 aRozell, Eric, A.1 aHitzler, Pascal1 aArko, Robert1 aChandler, Cynthia1 aWilson, Brian, D. uhttps://daselab.cs.ksu.edu/publications/leveraging-crowdsourcing-and-linked-open-data-geoscience-data-sharing-and-discovery00339nas a2200121 4500008004100000245001900041210001900060260001800079100002000097700001700117700002400134856005900158 2013 eng d00aLightweight KR0 aLightweight KR aArlington, VA1 aHitzler, Pascal1 aNoy, Natasha1 aMcGuinness, Deborah uhttps://daselab.cs.ksu.edu/publications/lightweight-kr00636nas a2200133 4500008004100000245004800041210004600089300001400135490000600149520026300155100002000418700002400438856004000462 2013 eng d00aLinked Data, Big Data, and the 4th Paradigm0 aLinked Data Big Data and the 4th Paradigm a233–2350 v43 aIt appears to be uncontroversial that Linked Data is part of the Big Data landscape. We even go a bit further and claim that Linked Data is an ideal testbed for researching some key Big Data challenges and to experience the 4th paradigm of science in action.1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttp://dx.doi.org/10.3233/SW-13011700717nas a2200193 4500008004100000245010800041210006900149260001600218300001200234490000900246100002000255700002400275700001600299700002000315700002000335700001900355700001700374856013200391 2013 eng d00aLinked Scientometrics: Designing Interactive Scientometrics with Linked Data and Semantic Web Reasoning0 aLinked Scientometrics Designing Interactive Scientometrics with bCEUR-WS.org a53–560 v10351 aMcKenzie, Grant1 aJanowicz, Krzysztof1 aHu, Yingjie1 aSengupta, Kunal1 aHitzler, Pascal1 aBlomqvist, Eva1 aGroza, Tudor uhttps://daselab.cs.ksu.edu/publications/linked-scientometrics-designing-interactive-scientometrics-linked-data-and-semantic-web00941nas a2200289 4500008004100000245008400041210006900125260001300194300001400207490000900221100001600230700002400246700002000270700002000290700002000310700001800330700001800348700002000366700002000386700001900406700003000425700001600455700002100471700001700492700002400509856011800533 2013 eng d00aA Linked-Data-Driven and Semantically-Enabled Journal Portal for Scientometrics0 aLinkedDataDriven and SemanticallyEnabled Journal Portal for Scie bSpringer a114–1290 v82191 aHu, Yingjie1 aJanowicz, Krzysztof1 aMcKenzie, Grant1 aSengupta, Kunal1 aHitzler, Pascal1 aAlani, Harith1 aKagal, Lalana1 aFokoue, Achille1 aGroth, Paul, T.1 aBiemann, Chris1 aParreira, Josiane, Xavier1 aAroyo, Lora1 aNoy, Natasha, F.1 aWelty, Chris1 aJanowicz, Krzysztof uhttps://daselab.cs.ksu.edu/publications/linked-data-driven-and-semantically-enabled-journal-portal-scientometrics00412nas a2200133 4500008004100000245003600041210003600077260001300113300001400126100002500140700002000165700001700185856007600202 2013 eng d00aLogical linked data compression0 aLogical linked data compression bSpringer a170–1841 aJoshi, Amit, Krishna1 aHitzler, Pascal1 aDong, Guozhu uhttps://daselab.cs.ksu.edu/publications/logical-linked-data-compression00421nas a2200133 4500008004100000245006600041210006200107300000800169490000600177100002000183700002400203700002000227856004000247 2013 eng d00aThe New Manuscript Review System for the Semantic Web Journal0 aNew Manuscript Review System for the Semantic Web Journal a1170 v41 aHitzler, Pascal1 aJanowicz, Krzysztof1 aSengupta, Kunal uhttp://dx.doi.org/10.3233/SW-13009501810nas a2200289 4500008004100000245006000041210005700101260001300158300001200171490000900183520099400192653001601186653002901202653000801231100001801239700002001257700002401277700002201301700002101323700002001344700002101364700001901385700002401404700001901428700002301447856005001470 2013 eng d00aAn Ontology Design Pattern for Cartographic Map Scaling0 aOntology Design Pattern for Cartographic Map Scaling bSpringer a76–930 v78823 aThe concepts of scale is at the core of cartographic abstraction and mapping. It defines which geographic phenomena should be displayed, which type of geometry and map symbol to use, which measures can be taken, as well as the degree to which features need to be exaggerated or spatially displaced. In this work, we present an ontology design pattern for map scaling using the Web Ontology Language (OWL) within a particular extension of the OWL RL profile. We explain how it can be used to describe scaling applications, to reason over scale levels, and geometric representations. We propose an axiomatization that allows us to impose meaningful constraints on the pattern, and, thus, to go beyond simple surface semantics. Interestingly, this includes several functional constraints currently not expressible in any of the OWL profiles. We show that for this specific scenario, the addition of such constraints does not increase the reasoning complexity which remains tractable.
10aMap Scaling10aOntology Design Patterns10aOWL1 aCarral, David1 aScheider, Simon1 aJanowicz, Krzysztof1 aVardeman, Charles1 aKrisnadhi, Adila1 aHitzler, Pascal1 aCimiano, Philipp1 aCorcho, Óscar1 aPresutti, Valentina1 aHollink, Laura1 aRudolph, Sebastian uhttp://dx.doi.org/10.1007/978-3-642-38288-8_601253nas a2200385 4500008004100000245010200041210006900143260001600212300001200228490000900240100002200249700001800271700002700289700002800316700001700344700002600361700001800387700001900405700001700424700001800441700002300459700002200482700002800504700001700532700002400549700002600573700002000599700002100619700002500640700002000665700001800685700001900703700001700722856012800739 2013 eng d00aOptique 1.0: Semantic Access to Big Data: The Case of Norwegian Petroleum Directorate's FactPages0 aOptique 10 Semantic Access to Big Data The Case of Norwegian Pet bCEUR-WS.org a65–680 v10351 aKharlamov, Evgeny1 aGiese, Martin1 aJiménez-Ruiz, Ernesto1 aSkjæveland, Martin, G.1 aSoylu, Ahmet1 aZheleznyakov, Dmitriy1 aBagosi, Timea1 aConsole, Marco1 aHaase, Peter1 aHorrocks, Ian1 aMarciuska, Sarunas1 aPinkel, Christoph1 aRodriguez-Muro, Mariano1 aRuzzi, Marco1 aSantarelli, Valerio1 aSavo, Domenico, Fabio1 aSengupta, Kunal1 aSchmidt, Michael1 aThorstensen, Evgenij1 aTrame, Johannes1 aWaaler, Arild1 aBlomqvist, Eva1 aGroza, Tudor uhttps://daselab.cs.ksu.edu/publications/optique-10-semantic-access-big-data-case-norwegian-petroleum-directorates-factpages02037nas a2200229 4500008004100000245004200041210004200083300001400125490000600139520143400145653002401579653001501603653002201618653000801640653002001648653001701668653002601685100002101711700001201732700002001744856004301764 2013 eng d00aParaconsistent OWL and Related Logics0 aParaconsistent OWL and Related Logics a395–4270 v43 aThe Web Ontology Language OWL is currently the most prominent formalism for representing ontologies in Semantic Web applications. OWL is based on description logics, and automated reasoners are used to infer knowledge implicitly present in OWL ontologies. However, because typical description logics obey the classical principle of explosion, reasoning over inconsistent ontologies is impossible in OWL. This is so despite the fact that inconsistencies are bound to occur in many realistic cases, e.g., when multiple ontologies are merged or when ontologies are created by machine learning or data mining tools. In this paper, we present four-valued paraconsistent description logics which can reason over inconsistencies. We focus on logics corresponding to OWL DL and its profiles. We present the logic SROIQ4, showing that it is both sound relative to classical SROIQ and that its embedding into SROIQ is consequence preserving. We also examine paraconsistent varieties of EL++, DL-Lite, and Horn-DLs. The general framework described here has the distinct advantage of allowing classical reasoners to draw sound but nontrivial conclusions from even inconsistent knowledge bases. Truth-value gaps and gluts can also be selectively eliminated from models (by inserting additional axioms into knowledge bases). If gaps but not gluts are eliminated, additional classical conclusions can be drawn without affecting paraconsistency.10aAutomated Deduction10aComplexity10aDescription Logic10aOWL10aParaconsistency10aSemantic Web10aWeb Ontology Language1 aMaier, Frederick1 aMa, Yue1 aHitzler, Pascal uhttp://dx.doi.org/10.3233/SW-2012-006600655nas a2200121 4500008004100000245020300041210006900244260001900313100003000332700002000362700002000382856013100402 2013 eng d00aProceedings of the Ninth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy'13, at the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 20130 aProceedings of the Ninth International Workshop on NeuralSymboli aBeijing, China1 aGarcez, Artur, S. d'Avila1 aHitzler, Pascal1 aLamb, Luís, C. uhttps://daselab.cs.ksu.edu/publications/proceedings-ninth-international-workshop-neural-symbolic-learning-and-reasoning-nesy1301470nas a2200205 4500008004100000245006600041210006600107300001400173490000700187520083300194653002001027653003301047653002901080653002801109653002901137100001801166700001601184700002001200856004401220 2013 eng d00aReasoning with Inconsistencies in Hybrid MKNF Knowledge Bases0 aReasoning with Inconsistencies in Hybrid MKNF Knowledge Bases a263–2900 v213 aThis paper is concerned with the handling of inconsistencies occurring in the combination of description logics and rules, especially in hybrid MKNF knowledge bases. More precisely, we present a paraconsistent semantics for hybrid MKNF knowledge bases (called para-MKNF knowledge bases) based on four-valued logic as proposed by Belnap. We also reduce this paraconsistent semantics to the stable model semantics via a linear transformation operator, which shows the relationship between the two semantics and indicates that the data complexity in our paradigm is not higher than that of classical reasoning. Moreover, we provide fixpoint operators to compute paraconsistent MKNF models, each suitable to different kinds of rules. At last we present the data complexity of instance checking in different paraMKNF knowledge bases.10aData complexity10aDescription logics and rules10aKnowledge representation10aNon-monotonic reasoning10aParaconsistent reasoning1 aHuang, Shasha1 aLi, Qingguo1 aHitzler, Pascal uhttp://dx.doi.org/10.1093/jigpal/jzs04301222nas a2200205 4500008004100000245006500041210006300106260001900169300001000188520058100198100002000779700001500799700001500814700002000829700002300849700001600872700001700888700001500905856009600920 2013 eng d00aScale reasoning with fuzzy-EL+ ontologies based on MapReduce0 aScale reasoning with fuzzyEL ontologies based on MapReduce aBeijing, China a87-933 aFuzzy extension of Description Logics (DLs) allows the formal representation and handling of fuzzy or vague knowledge. In this paper, we consider the problem of reasoning with fuzzy-EL+, which is a fuzzy extension of EL+. We first identify the challenges and present revised completion classification rules for fuzzy-EL+ that can be handled by MapReduce programs. We then propose an algorithm for scale reasoning with fuzzy-EL+ ontologies using MapReduce. Some preliminary experimental results are provided to show the scalability of our algorithm.
1 aZhou, Zhangquan1 aQi, Guilin1 aLiu, Chang1 aHitzler, Pascal1 aMutharaju, Raghava1 aGodo, Lluis1 aPrade, Henri1 aQi, Guilin uhttps://daselab.cs.ksu.edu/publications/scale-reasoning-fuzzy-el-ontologies-based-mapreduce00590nas a2200133 4500008004100000245010200041210006900143260002900212100002400241700002300265700002000288700002400308856012400332 2013 eng d00aSemantics for Big Data: Papers from the AAAI Symposium, November 15-17, 2013, Arlington, Virginia0 aSemantics for Big Data Papers from the AAAI Symposium November 1 aArlington, Virginia, USA1 avan Harmelen, Frank1 aHendler, James, A.1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttps://daselab.cs.ksu.edu/publications/semantics-big-data-papers-aaai-symposium-november-15-17-2013-arlington-virginia00440nas a2200097 4500008004100000245007100041210006900112260003400181100002200215856010500237 2013 eng d00aSide Effects Recognition as Implicit Opinion Words in Drug Reviews0 aSide Effects Recognition as Implicit Opinion Words in Drug Revie bUniversiti Teknologi Malaysia1 aEbrahimi, Monireh uhttps://daselab.cs.ksu.edu/publications/side-effects-recognition-implicit-opinion-words-drug-reviews00387nas a2200121 4500008004100000245005600041210005600097300001400153100001500167700001800182700002000200856004500220 2013 eng d00aSROIQ Syntax Approximation by Using Nominal Schemas0 aSROIQ Syntax Approximation by Using Nominal Schemas a988–9991 aWang, Cong1 aCarral, David1 aHitzler, Pascal uhttp://ceur-ws.org/Vol-1014/paper_31.pdf00468nas a2200121 4500008004100000245005300041210005300094260003200147520003500179100002300214700002000237856008900257 2013 eng d00aString Similarity Metrics for Ontology Alignment0 aString Similarity Metrics for Ontology Alignment aSydney, AustraliabSpringer3 a
Linked Data (LD) has been an active research area for more than 6 years and many aspects about publishing, retrieving, linking, and cleaning Linked Data have been investigated. There seems to be a broad and general agreement that in principle LD datasets can be very useful for solving a wide variety of problems ranging from practical industrial analytics to highly specific research problems. Having these notions in mind, we started exploring the use of notable LD datasets such as DBpedia, Freebase, Geonames and others for a commercial application. However, it turns out that using these datasets in realistic settings is not always easy. Surprisingly, in many cases the underlying issues are not technical but legal barriers erected by the LD data publishers. In this paper we argue that these barriers are often not justified, detrimental to both data publishers and users, and are often built without much consideration of their consequences.
1 aJain, Prateek1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aVenkatramani, Chitra uhttps://daselab.cs.ksu.edu/publications/there%E2%80%99s-no-money-linked-data01189nas a2200181 4500008004100000245009500041210006900136260002700205300001200232520058900244100002400833700002000857700002200877700002500899700001600924700002000940856004700960 2013 eng d00aThoughts on the Complex Relation Between Linked Data, Semantic Annotations, and Ontologies0 aThoughts on the Complex Relation Between Linked Data Semantic An aSan Francisco, CAbACM a41–443 aThe relation between data, annotations, and schemata seems straightforward at first: Data are annotated with additional meta information according to some schemata in order to expose additional non-intrinsic characteristics relevant to the meaningful interpretation of said data. However, on closer examination, things are not as simple. Focusing on geo-information retrieval, we will try to disentangle the aforementioned relations. We will report from our own experience and from observations gathered by editing papers about ontologies and Linked Data for the Semantic Web journal.1 aJanowicz, Krzysztof1 aHitzler, Pascal1 aBennett, Paul, N.1 aGabrilovich, Evgeniy1 aKamps, Jaap1 aKarlgren, Jussi uhttp://doi.acm.org/10.1145/2513204.251321801105nas a2200169 4500008004100000245009900041210006900140300001200209520055900221653002300780653000900803653002000812100001800832700001500850700002000865856005000885 2013 eng d00aTowards an Efficient Algorithm to Reason over Description Logics Extended with Nominal Schemas0 aTowards an Efficient Algorithm to Reason over Description Logics a65–793 aExtending description logics with so-called nominal schemas has been shown to be a major step towards integrating description logics with rules paradigms. However, establishing efficient algorithms for reasoning with nominal schemas has so far been a challenge. In this paper, we present an algorithm to reason with the description logic fragment ELROVn, a fragment that extends EL++ with nominal schemas. We also report on an implementation and experimental evaluation of the algorithm, which shows that our approach is indeed rather efficient.
10adescription logics10aEL++10aNominal Schemas1 aCarral, David1 aWang, Cong1 aHitzler, Pascal uhttp://dx.doi.org/10.1007/978-3-642-39666-3_600520nam a2200169 4500008004100000245002600041210002600067260003000093100002000123700002200143700002300165700001300188700001500201700001700216700001500233856010200248 2013 eng d00a语义Web技术基础0 a语义Web技术基础 bTsinghua University Press1 aHitzler, Pascal1 aKrötzsch, Markus1 aRudolph, Sebastian1 aYu, Yong1 aQi, Guilin1 aWang, Haofen1 aLiu, Chang uhttps://daselab.cs.ksu.edu/publications/%E8%AF%AD%E4%B9%89web%E6%8A%80%E6%9C%AF%E5%9F%BA%E7%A1%8000568nas a2200181 4500008004100000245004900041210004800090260001300138300001400151100002500165700001800190700002000208700001900228700001700247700001600264700002000280856008600300 2012 eng d00aAlignment-based querying of linked open data0 aAlignmentbased querying of linked open data bSpringer a807–8241 aJoshi, Amit, Krishna1 aJain, Prateek1 aHitzler, Pascal1 aYeh, Peter, Z.1 aVerma, Kunal1 aSheth, Amit1 aDamova, Mariana uhttps://daselab.cs.ksu.edu/publications/alignment-based-querying-linked-open-data00514nas a2200157 4500008004100000245007100041210006900112300001300181490000600194100001900200700002400219700002000243700002400263700002500287856004400312 2012 eng d00aCognitive Approaches for the Semantic Web (Dagstuhl Seminar 12221)0 aCognitive Approaches for the Semantic Web Dagstuhl Seminar 12221 a93–1160 v21 aGentner, Dedre1 avan Harmelen, Frank1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aKühnberger, Kai-Uwe uhttp://dx.doi.org/10.4230/DagRep.2.5.9300397nas a2200109 4500008004100000245007700041210006900118300001400187100001500201700002000216856005100236 2012 eng d00aConsequence-Based Procedure for Description Logics with Self-Restriction0 aConsequenceBased Procedure for Description Logics with SelfRestr a169–1801 aWang, Cong1 aHitzler, Pascal uhttp://dx.doi.org/10.1007/978-1-4614-6880-6_1501579nas a2200133 4500008004100000245004200041210003800083300001400121490000600135520121700141100002401358700002001382856004301402 2012 eng d00aThe Digital Earth as Knowledge Engine0 aDigital Earth as Knowledge Engine a213–2210 v33 aThe Digital Earth aims at developing a digital representation of the planet. It is motivated by the need for integrating and interlinking vast geo-referenced, multi-thematic, and multi-perspective knowledge archives that cut through domain boundaries. Complex scientific questions cannot be answered from within one domain alone but span over multiple scientific disciplines. For instance, studying disease dynamics for prediction and policy making requires data and models from a diverse body of science ranging from medical science and epidemiology over geography and economics to mining the social Web. The naive assumption that such problems can simply be addressed by more data with a higher spatial, temporal, and thematic resolution fails as long as this more on data is not supported by more knowledge on how to combine and interpret the data. This makes semantic interoperability a core research topic of data-intensive science. While the Digital Earth vision includes processing services, it is, at its very core, a data archive and infrastructure. We propose to redefine the Digital Earth as a knowledge engine and discuss what the Semantic Web has to offer in this context and to Big Data in general.1 aJanowicz, Krzysztof1 aHitzler, Pascal uhttp://dx.doi.org/10.3233/SW-2012-007001166nas a2200157 4500008004100000245003800041210003800079300001400117520074700131653002300878653000800901653001000909100001800919700002000937856005100957 2012 eng d00aExtending Description Logic Rules0 aExtending Description Logic Rules a345–3593 aDescription Logics – the logics underpinning the Web Ontology Language OWL – and rules are currently the most prominent paradigms used for modeling knowledge for the Semantic Web. While both of these approaches are based on classical logic, the paradigms also differ significantly, so that naive combinations result in undesirable properties such as undecidability. Recent work has shown that many rules can in fact be expressed in OWL. In this paper we extend this work to include some types of rules previously excluded. We formally define a set of first order logic rules, C-Rules, which can be expressed within OWL extended with role conjunction. We also show that the use of nominal schemas results in even broader coverage.
10adescription logics10aOWL10aRules1 aCarral, David1 aHitzler, Pascal uhttp://dx.doi.org/10.1007/978-3-642-30284-8_3000961nas a2200109 4500008004100000245005700041210005600098260003300154520055700187100002300744856008400767 2012 eng d00aHow I Would Like Semantic Web To Be, For My Children0 aHow I Would Like Semantic Web To Be For My Children aBoston, MA, USAbCEUR-WS.org3 aSemantic Web, since its inception, has gone through lot of developments in its relatively nascent existence; right from people’s perception, to the standards and to its adoption by the industry and more importantly by the scientific community. This impressive growth only seems to increase. In this paper, we project this growth to the next 10 years and highlight some of the facets on which Semantic Web could have a major impact on. We also present the challenges that Semantic Web and its community has to deal with in order to get there.
1 aMutharaju, Raghava uhttps://daselab.cs.ksu.edu/publications/how-i-would-semantic-web-be-my-children01221nas a2200169 4500008004100000245007100041210006800112260002500180490000800205520069600213100001800909700002100927700002000948700001800968700002200986856004301008 2012 eng d00aIntegrating {OWL} and Rules: A Syntax Proposal for Nominal Schemas0 aIntegrating OWL and Rules A Syntax Proposal for Nominal Schemas bCEUR-WS.orgc05/20120 v8493 aThis paper proposes an addition to OWL 2 syntax to incorporate nominal schemas, which is a new description-logic style extension of OWL 2 which was recently proposed, and which makes is possible to express “variable nominal classes” within axioms in an OWL 2 ontology. Nominal schemas make it possible to express DL-safe rules of arbitrary arity within the extended OWL paradigm, hence covering the well-known DL-safe SWRL language. To express this feature, we extend OWL 2 syntax to include necessary and minimal modifications to both Functional and Manchester syntax grammars and mappings from these two syntaxes to Turtle/RDF. We also include several examples to clarify the proposal.1 aCarral, David1 aKrisnadhi, Adila1 aHitzler, Pascal1 aKlinov, Pavel1 aHorridge, Matthew uhttp://ceur-ws.org/Vol-849/paper_6.pdf01309nas a2200193 4500008004100000245005800041210005800099260003000157300001400187490000900201520072100210100002400931700002000955700002100975700002300996700002801019700001701047856005101064 2012 eng d00aKey Ingredients For Your Next Semantics Elevator Talk0 aKey Ingredients For Your Next Semantics Elevator Talk aFlorence, ItalybSpringer a213–2200 v75183 a2012 brought a major change to the semantics research community. Discussions on the use and benefits of semantic technologies are shifting away from the why to the how. Surprisingly this more in stakeholder interest is not accompanied by a more detailed understanding of what semantics research is about. Instead of blaming others for their (wrong) expectations, we need to learn how to emphasize the paradigm shift proposed by semantics research while abstracting from technical details and advocate the added value in a way that relates to the immediate needs of individual stakeholders without overselling. This paper highlights some of the major ingredients to prepare your next Semantics Elevator Talk.
1 aJanowicz, Krzysztof1 aHitzler, Pascal1 aCastano, Silvana1 aVassiliadis, Panos1 aLakshmanan, Laks, V. S.1 aLee, Mong-Li uhttp://dx.doi.org/10.1007/978-3-642-33999-8_2701048nas a2200169 4500008004100000245001700041210001700058260001700075520060700092100001800699700002500717700002100742700002300763700002000786700001500806856005700821 2012 eng d00aKonf Connect0 aKonf Connect aLyon, France3 aWe present an application called Konf-Connect to improve the conference attending experience of the people who attend a conference. This tool provides search facilities to nd people with similar interests. The application makes use of Semantic Web dog food dataset to gather information regarding the conference at hand. This is helpful for people attending the conference who are looking for networking opportunities with people having expertise in the specic areas of interest. The application can also be extended to be used as general purpose expert search system.
1 aCarral, David1 aJoshi, Amit, Krishna1 aKrisnadhi, Adila1 aMutharaju, Raghava1 aSengupta, Kunal1 aWang, Cong uhttps://daselab.cs.ksu.edu/publications/konf-connect00546nas a2200169 4500008004100000245006900041210006600110300001400176653001700190653002300207653002900230653000800259100001800267700002400285700002000309856004700329 2012 eng d00aA logical geo-ontology design pattern for quantifying over types0 alogical geoontology design pattern for quantifying over types a239–24810aBiodiversity10adescription logics10aOntology Design Patterns10aOWL1 aCarral, David1 aJanowicz, Krzysztof1 aHitzler, Pascal uhttp://doi.acm.org/10.1145/2424321.242435201920nas a2200229 4500008004100000245007300041210006900114260002800183300001200211520122300223653002701446653001401473653002101487100001801508700002001526700001701546700001901563700001601582700002201598700002301620856004701643 2012 eng d00aMoving beyond SameAs with PLATO: Partonomy detection for Linked Data0 aMoving beyond SameAs with PLATO Partonomy detection for Linked D aMilwaukee, WI, USAbACM a33–423 aThe Linked Open Data (LOD) Cloud has gained significant traction over the past few years. With over 275 interlinked datasets across diverse domains such as life science, geography, politics, and more, the LOD Cloud has the potential to support a variety of applications ranging from open domain question answering to drug discovery.
Despite its significant size (approx. 30 billion triples), the data is relatively sparely interlinked (approx. 400 million links). A semantically richer LOD Cloud is needed to fully realize its potential. Data in the LOD Cloud are currently interlinked mainly via the owl:sameAs property, which is inadequate for many applications. Additional properties capturing relations based on causality or partonomy are needed to enable the answering of complex questions and to support applications.
In this paper, we present a solution to enrich the LOD Cloud by automatically detecting partonomic relationships, which are well-established, fundamental properties grounded in linguistics and philosophy. We empirically evaluate our solution across several domains, and show that our approach performs well on detecting partonomic properties between LOD Cloud data.
10aLinked Open Data Cloud10aMereology10aPart of Relation1 aJain, Prateek1 aHitzler, Pascal1 aVerma, Kunal1 aYeh, Peter, Z.1 aSheth, Amit1 aMunson, Ethan, V.1 aStrohmaier, Markus uhttp://doi.acm.org/10.1145/2309996.231000401047nas a2200133 4500008004100000245007300041210006900114300001000183490000700193520056800200100002400768700002000792856010100812 2012 eng d00aOpen and transparent: the review process of the Semantic Web journal0 aOpen and transparent the review process of the Semantic Web jour a48-550 v253 aWhile open access is established in the world of academic publishing, open reviews are rare. The Semantic Web journal goes further than just open review by implementing an open and transparent review process in which reviews are publicly available, the assigned editors and reviewers are known by name, and are published together with accepted manuscripts. In this article we introduce the steps to realize such a process from the conceptual design, over the implementation, a overview of the results so far, and up to lessons learned.
1 aJanowicz, Krzysztof1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/open-and-transparent-review-process-semantic-web-journal00499nas a2200157 4500008004100000245005700041210005400098260001500152300002300167100002000190700002200210700001800232700003100250700002300281856003700304 2012 eng d00aOWL 2 Web Ontology Language: Primer (Second Edition)0 aOWL 2 Web Ontology Language Primer Second Edition c12/11/2012 aW3C Recommendation1 aHitzler, Pascal1 aKrötzsch, Markus1 aParsia, Bijan1 aPatel-Schneider, Peter, F.1 aRudolph, Sebastian uhttp://www.w3.org/TR/owl2-primer00646nas a2200121 4500008004100000245019200041210006900233260002000302100003000322700002000352700002000372856013200392 2012 eng d00aProceedings of the Eighth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy'12, at the 26th Conference on Artificial Intelligence, AAAI-12, Toronto, Canada, July 20120 aProceedings of the Eighth International Workshop on NeuralSymbol aToronto, Canada1 aGarcez, Artur, S. d'Avila1 aHitzler, Pascal1 aLamb, Luís, C. uhttps://daselab.cs.ksu.edu/publications/proceedings-eighth-international-workshop-neural-symbolic-learning-and-reasoning-nesy1200493nas a2200145 4500008003900000245004500039210004500084260002200129490004100151100001500192700002100207700001800228700002000246856008100266 2012 d00aReasoning Approaches for Nominal Schemas0 aReasoning Approaches for Nominal Schemas aNara, JapanbJIST0 vPoster and Demonstration Proceedings1 aWang, Cong1 aKrisnadhi, Adila1 aCarral, David1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/reasoning-approaches-nominal-schemas01175nas a2200265 4500008004100000245005600041210005400097260003500151300001400186490000800200520040900208100002000617700001500637700001500652700002000667700002300687700001800710700002500728700001900753700002100772700002000793700002000813700002400833856005200857 2012 eng d00aReasoning with Fuzzy-EL+ Ontologies Using MapReduce0 aReasoning with FuzzyEL Ontologies Using MapReduce aMontpellier, FrancebIOS Press a933–9340 v2423 aFuzzy extension of Description Logics (DLs) allows the formal representation and handling of fuzzy knowledge. In this paper, we consider fuzzy-EL+, which is a fuzzy extension of EL+. We first present revised completion rules for fuzzy-EL+ that can be handled by MapReduce programs. We then propose an algorithm for scale reasoning with fuzzy-EL+ ontologies based on MapReduce.
1 aZhou, Zhangquan1 aQi, Guilin1 aLiu, Chang1 aHitzler, Pascal1 aMutharaju, Raghava1 aDe Raedt, Luc1 aBessière, Christian1 aDubois, Didier1 aDoherty, Patrick1 aFrasconi, Paolo1 aHeintz, Fredrik1 aLucas, Peter, J. F. uhttp://dx.doi.org/10.3233/978-1-61499-098-7-93301833nas a2200253 4500008004100000245005100041210004900092260003900141300001200180490000900192520112700201653002301328653000801351653001001359100002001369700001801389700002001407700002101427700002101448700001501469700002201484700002201506856005101528 2012 eng d00aRecent Advances in Integrating {OWL} and Rules0 aRecent Advances in Integrating OWL and Rules aAustria, ViennabSpringerc09/2012 a225-2280 v74973 aAs part of the quest for a unifying logic for the Semantic Web Technology Stack, a central issue is finding suitable ways of integrating description logics based on the Web Ontology Language (OWL) with rule-based approaches based on logic programming. Such integration is difficult since naive approaches typically result in the violation of one or more desirable design principles. For example, while both OWL 2 DL and RIF Core (a dialect of the Rule Interchange Format RIF) are decidable, their naive union is not, unless carefully chosen syntactic restrictions are applied. We report on recent advances and ongoing work by the authors in integrating OWL and rulesWe take an OWL-centric perspective, which means that we take OWL 2 DL as a starting point and pursue the question of how features of rulebased formalisms can be added without jeopardizing decidability. We also report on incorporating the closed world assumption and on reasoning algorithms. This paper essentially serves as an entry point to the original papers, to which we will refer throughout, where detailed expositions of the results can be found.10adescription logics10aOWL10aRules1 aKnorr, Matthias1 aCarral, David1 aHitzler, Pascal1 aKrisnadhi, Adila1 aMaier, Frederick1 aWang, Cong1 aKrötzsch, Markus1 aStraccia, Umberto uhttp://dx.doi.org/10.1007/978-3-642-33203-6_2001450nas a2200241 4500008004100000245006500041210006400106260003500170300001400205490000800219520072100227100002000948700002000968700002100988700001801009700002501027700001901052700002101071700002001092700002001112700002401132856005201156 2012 eng d00aReconciling OWL and Non-monotonic Rules for the Semantic Web0 aReconciling OWL and Nonmonotonic Rules for the Semantic Web aMontpellier, FrancebIOS Press a474–4790 v2423 aWe propose a description logic extending SROIQ (the description logic underlying OWL 2 DL) and at the same time encompassing some of the most prominent monotonic and nonmonotonic rule languages, in particular Datalog extended with the answer set semantics. Our proposal could be considered a substantial contribution towards fulfilling the quest for a unifying logic for the Semantic Web. As a case in point, two non-monotonic extensions of description logics considered to be of distinct expressiveness until now are covered in our proposal. In contrast to earlier such proposals, our language has the “look and feel” of a description logic and avoids hybrid or first-order syntaxes.
1 aKnorr, Matthias1 aHitzler, Pascal1 aMaier, Frederick1 aDe Raedt, Luc1 aBessière, Christian1 aDubois, Didier1 aDoherty, Patrick1 aFrasconi, Paolo1 aHeintz, Fredrik1 aLucas, Peter, J. F. uhttp://dx.doi.org/10.3233/978-1-61499-098-7-47400949nas a2200325 4500008004100000245005000041210005000091300001400141490000700155100001900162700001700181700002300198700001700221700003000238700002000268700001900288700002100307700001500328700002000343700002000363700002200383700002200405700002300427700002200450700002700472700001600499700002100515700002000536856006700556 2012 eng d00aReports of the AAAI 2012 Conference Workshops0 aReports of the AAAI 2012 Conference Workshops a119–1270 v331 aAgrawal, Vikas1 aBaier, Jorge1 aBekris, Kostas, E.1 aChen, Yiling1 aGarcez, Artur, S. d'Avila1 aHitzler, Pascal1 aHaslum, Patrik1 aJannach, Dietmar1 aLaw, Edith1 aLécué, Freddy1 aLamb, Luís, C.1 aMatuszek, Cynthia1 aPalacios, Héctor1 aSrivastava, Biplav1 aShastri, Lokendra1 aSturtevant, Nathan, R.1 aStern, Roni1 aTellex, Stefanie1 aVassos, Stavros uhttp://www.aaai.org/ojs/index.php/aimagazine/article/view/244400947nas a2200325 4500008004100000245005000041210005000091300001200141490000700153100001900160700001700179700002300196700001700219700003000236700002000266700001900286700002100305700001500326700002000341700002000361700002200381700002200403700002300425700002200448700002700470700001600497700002100513700002000534856006700554 2012 eng d00aReports of the AAAI 2012 Conference Workshops0 aReports of the AAAI 2012 Conference Workshops a119-1270 v331 aAgrawal, Vikas1 aBaier, Jorge1 aBekris, Kostas, E.1 aChen, Yiling1 aGarcez, Artur, S. d'Avila1 aHitzler, Pascal1 aHaslum, Patrik1 aJannach, Dietmar1 aLaw, Edith1 aLécué, Freddy1 aLamb, Luís, C.1 aMatuszek, Cynthia1 aPalacios, Héctor1 aSrivastava, Biplav1 aShastri, Lokendra1 aSturtevant, Nathan, R.1 aStern, Roni1 aTellex, Stefanie1 aVassos, Stavros uhttp://www.aaai.org/ojs/index.php/aimagazine/article/view/244400387nas a2200109 4500008004100000245007100041210006900112300001100181100001500192700002000207856005000227 2012 eng d00aA Resolution Procedure for Description Logics with Nominal Schemas0 aResolution Procedure for Description Logics with Nominal Schemas a1–161 aWang, Cong1 aHitzler, Pascal uhttp://dx.doi.org/10.1007/978-3-642-37996-3_101201nas a2200169 4500008004100000245003400041210003400075520071900109100002000828700002400848700002100872700001500893700001600908700001900924700001700943856007100960 2012 eng d00aSemantic Aspects of EarthCube0 aSemantic Aspects of EarthCube3 aIn this document, we give a high-level overview of selected Semantic (Web) technologies, methods, and other important considerations, that are relevant for the success of EarthCube. The goal of this initial document is to provide entry points and references for discussions between the Semantic Technologies experts and the domain experts within EarthCube. The selected topics are intended to ground the EarthCube roadmap in the state of the art in semantics research and ontology engineering.
We anticipate that this document will evolve as EarthCube progresses. Indeed, all EarthCube parties are asked to provide topics of importance that should be treated in future versions of this document.
1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aBerg-Cross, Gary1 aObrst, Leo1 aSheth, Amit1 aFinin, Timothy1 aCruz, Isabel uhttps://daselab.cs.ksu.edu/publications/semantic-aspects-earthcube02034nas a2200373 4500008004100000245004300041210004300084260002400127520100800151100002101159700001701180700001501197700001501212700001801227700002001245700001401265700002401279700001601303700001901319700002001338700001501358700002201373700001601395700001901411700001801430700001901448700002001467700002401487700001601511700001801527700001601545700002001561856007901581 2012 eng d00aSemantics and Ontologies for EarthCube0 aSemantics and Ontologies for EarthCube aColumbus, Ohio, USA3 aSemantic technologies and ontologies play an increasing role in scientific workflow systems and knowledge infrastructures. While ontologies are mostly used for the semantic annotation of metadata, semantic technologies enable searching metadata catalogs beyond simple keywords, with some early evidence of semantics used for data translation. However, the next generation of distributed and interdisciplinary knowledge infrastructures will require capabilities beyond simple subsumption reasoning over subclass relations. In this work, we report from the EarthCube Semantics Community by highlighting which role semantics and ontologies should play in the EarthCube knowledge infrastructure. We target the interested domain scientist and, thus, introduce the value proposition of semantic technologies in a non-technical language. Finally, we commit ourselves to some guiding principles for the successful implementation and application of semantic technologies and ontologies within EarthCube.
1 aBerg-Cross, Gary1 aCruz, Isabel1 aDean, Mike1 aFinin, Tim1 aGahegan, Mark1 aHitzler, Pascal1 aHua, Hook1 aJanowicz, Krzysztof1 aLi, Naicong1 aMurphy, Philip1 aNordgren, Bryce1 aObrst, Leo1 aSchildhauer, Mark1 aSheth, Amit1 aSinha, Krishna1 aThessen, Anne1 aWiegand, Nancy1 aZaslavsky, Ilya1 aJanowicz, Krzysztof1 aKessler, C.1 aKauppinen, T.1 aKolas, Dave1 aScheider, Simon uhttps://daselab.cs.ksu.edu/publications/semantics-and-ontologies-earthcube01139nas a2200169 4500008004100000245006800041210006600109260002200175300001200197490000900209520056900218100002100787700002000808700002200828700002200850856009700872 2012 eng d00aA Tableau Algorithm for Description Logics with Nominal Schemas0 aTableau Algorithm for Description Logics with Nominal Schemas bSpringerc09/2012 a234-2370 v74973 aWe present a tableau algorithm for the description logic ALCOV. This description logic is obtained by extending the description logic ALCO with the expressive nominal schema construct that enables DL-safe datalog with predicates of arbitrary arity to be covered within the description logic framework. The tableau algorithm provides a basis to implement a delayed grounding strategy which was not facilitated by earlier versions of decision procedures for satisfiability in expressive description logics with nominal schemas.
1 aKrisnadhi, Adila1 aHitzler, Pascal1 aKrötzsch, Markus1 aStraccia, Umberto uhttps://daselab.cs.ksu.edu/publications/tableau-algorithm-description-logics-nominal-schemas00385nas a2200109 4500008004100000245004400041210004400085100002500129700002000154700001700174856008400191 2012 eng d00aTowards logical linked data compression0 aTowards logical linked data compression1 aJoshi, Amit, Krishna1 aHitzler, Pascal1 aDong, Guozhu uhttps://daselab.cs.ksu.edu/publications/towards-logical-linked-data-compression01393nas a2200181 4500008004100000245011800041210006900159490000600228520078700234653001201021653002201033653002301055653002101078100002301099700002201122700002001144856004701164 2012 eng d00aType-Elimination-Based Reasoning for the Description Logic SHIQbs using Decision Diagrams and Disjunctive Datalog0 aTypeEliminationBased Reasoning for the Description Logic SHIQbs 0 v83 aWe propose a novel, type-elimination-based method for standard reasoning in the description logic SHIQbs extended by DL-safe rules. To this end, we first establish a knowledge compilation method converting the terminological part of an ALCIb knowledge base into an ordered binary decision diagram (OBDD) that represents a canonical model. This OBDD can in turn be transformed into disjunctive Datalog and merged with the assertional part of the knowledge base in order to perform combined reasoning. In order to leverage our technique for full SHIQbs, we provide a stepwise reduction from SHIQbs to ALCIb that preserves satisfiability and entailment of positive and negative ground facts. The proposed technique is shown to be worst-case optimal w.r.t. combined and data complexity.10adatalog10adecision diagrams10adescription logics10atype elimination1 aRudolph, Sebastian1 aKrötzsch, Markus1 aHitzler, Pascal uhttp://dx.doi.org/10.2168/LMCS-8(1:12)201201724nas a2200301 4500008004100000245006700041210006700108260003000175300001400205490000900219520081100228653002601039653002801065653001101093100002301104700002901127700001701156700001701173700002101190700002201211700002301233700003001256700002301286700002001309700002301329700001901352856005101371 2012 eng d00aVery Large Scale OWL Reasoning through Distributed Computation0 aVery Large Scale OWL Reasoning through Distributed Computation aBoston, MA, USAbSpringer a407–4140 v76503 aDue to recent developments in reasoning algorithms of the various OWL profiles, the classification time for an ontology has come down drastically. For all of the popular reasoners, in order to process an ontology, an implicit assumption is that the ontology should fit in primary memory. The memory requirements for a reasoner are already quite high, and considering the ever increasing size of the data to be processed and the goal of making reasoning Web scale, this assumption becomes overly restrictive. In our work, we study several distributed classification approaches for the description logic EL+ (a fragment of OWL 2 EL profile). We present the lessons learned from each approach, our current results, and plans for future work.
10aDistributed Reasoning10aOntology Classification10aOWL EL1 aMutharaju, Raghava1 aCudré-Mauroux, Philippe1 aHeflin, Jeff1 aSirin, Evren1 aTudorache, Tania1 aEuzenat, Jérôme1 aHauswirth, Manfred1 aParreira, Josiane, Xavier1 aHendler, James, A.1 aSchreiber, Guus1 aBernstein, Abraham1 aBlomqvist, Eva uhttp://dx.doi.org/10.1007/978-3-642-35173-0_3001446nas a2200313 4500008004100000020002200041245008300063210006900146260001700215300001200232520051700244653001200761653002200773653003100795653001000826653001700836653002600853100002200879700002100901700002100922700002000943700002600963700002400989700001601013700002101029700001901050700001601069856004701085 2011 eng d a978-1-4503-0632-400aA Better Uncle for {OWL}: Nominal Schemas for Integrating Rules and Ontologies0 aBetter Uncle for OWL Nominal Schemas for Integrating Rules and O bACMc03/2011 a645-6543 aWe propose a description-logic style extension of OWL 2 with nominal schemas which can be used like "variable nominal classes" within axioms. This feature allows ontology languages to express arbitrary DL-safe rules (as expressible in SWRL or RIF) in their native syntax. We show that adding nominal schemas to OWL 2 does not increase the worst-case reasoning complexity, and we identify a novel tractable language SROELV3(\cap, x) that is versatile enough to capture the lightweight languages OWL EL and OWL RL.10adatalog10aDescription Logic10aSemantic Web Rule Language10aSROIQ10atractability10aWeb Ontology Language1 aKrötzsch, Markus1 aMaier, Frederick1 aKrisnadhi, Adila1 aHitzler, Pascal1 aSrinivasan, Sadagopan1 aRamamritham, Krithi1 aKumar, Arun1 aRavindra, M., P.1 aBertino, Elisa1 aKumar, Ravi uhttp://doi.acm.org/10.1145/1963405.196349602117nas a2200145 4500008004100000245007000041210006900111300001600180490000700196520167700203100001201880700001501892700002001907856004401927 2011 eng d00aComputing Inconsistency Measure based on Paraconsistent Semantics0 aComputing Inconsistency Measure based on Paraconsistent Semantic a1257–12810 v213 aMeasuring inconsistency in knowledge bases has been recognized as an important problem in several research areas. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. However, existing methods suffer from two limitations: 1) They are mostly restricted to propositional knowledge bases; 2) Very few of them discuss computational aspects of computing inconsistency measures. In this paper, we try to solve these two limitations by exploring algorithms for computing an inconsistency measure of first-order knowledge bases. After introducing a four-valued semantics for first-order logic, we define an inconsistency measure of a first-order knowledge base, which is a sequence of inconsistency degrees. We then propose a precise algorithm to compute our inconsistency measure. We show that this algorithm reduces the computation of the inconsistency measure to classical satisfiability checking. This is done by introducing a new semantics, named S[n]-4 semantics, which can be calculated by invoking a classical SAT solver. Moreover, we show that this auxiliary semantics also gives a direct way to compute upper and lower bounds of inconsistency degrees. That is, it can be easily revised to compute approximating inconsistency measures. The approximating inconsistency measures converge to the precise values if enough resources are available. Finally, by some nice properties of the S[n]-4 semantics, we show that some upper and lower bounds can be computed in P-time, which says that the problem of computing these approximating inconsistency measures is tractable.
1 aMa, Yue1 aQi, Guilin1 aHitzler, Pascal uhttp://dx.doi.org/10.1093/logcom/exq05302232nas a2200289 4500008004100000245009000041210006900131260003900200300001200239490000900251520133400260100001801594700001901612700001701631700002701648700002001675700002001695700001601715700002301731700002101754700003101775700001801806700002601824700002401850700001801874856005001892 2011 eng d00aContextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton0 aContextual Ontology Alignment of LOD with an Upper Ontology A Ca aHeraklion, Crete, GreecebSpringer a80–920 v66433 aThe Linked Open Data (LOD) is a major milestone towards realizing the Semantic Web vision, and can enable applications such as robust Question Answering (QA) systems that can answer queries requiring multiple, disparate information sources. However, realizing these applications requires relationships at both the schema and instance level, but currently the LOD only provides relationships for the latter. To address this limitation, we present a solution for automatically finding schema-level links between two LOD ontologies – in the sense of ontology alignment. Our solution, called BLOOMS+, extends our previous solution (i.e. BLOOMS) in two significant ways. BLOOMS+ 1) uses a more sophisticated metric to determine which classes between two ontologies to align, and 2) considers contextual information to further support (or reject) an alignment. We present a comprehensive evaluation of our solution using schema-level mappings from LOD ontologies to Proton (an upper level ontology) – created manually by human experts for a real world application called FactForge. We show that our solution performed well on this task. We also show that our solution significantly outperformed existing ontology alignment solutions (including our previously published work on BLOOMS) on this same task.
1 aJain, Prateek1 aYeh, Peter, Z.1 aVerma, Kunal1 aVasquez, Reymonrod, G.1 aDamova, Mariana1 aHitzler, Pascal1 aSheth, Amit1 aAntoniou, Grigoris1 aGrobelnik, Marko1 aSimperl, Elena, Paslaru Bo1 aParsia, Bijan1 aPlexousakis, Dimitris1 aDe Leenheer, Pieter1 aPan, Jeff, Z. uhttp://dx.doi.org/10.1007/978-3-642-21034-1_601632nas a2200217 4500008004100000245009000041210006900131300001600200490000800216520093900224653002201163653002901185653002201214653002801236653001501264653001701279100002001296700002701316700002001343856005101363 2011 eng d00aLocal Closed World Reasoning with Description Logics under the Well-Founded Semantics0 aLocal Closed World Reasoning with Description Logics under the W a1528–15540 v1753 aAn important question for the upcoming Semantic Web is how to best combine open world ontology languages, such as the OWL-based ones, with closed world rule-based languages. One of the most mature proposals for this combination is known as hybrid MKNF knowledge bases [52], and it is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. In this paper we propose a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based semantics and the traditional Well-Founded Semantics for logic programs. Moreover, our proposal allows for the detection of inconsistencies, possibly occurring in tightly integrated ontology axioms and rules, with only little additional effort. We also identify tractable fragments of the resulting language.
10aDescription Logic10aKnowledge representation10aLogic Programming10aNon-monotonic reasoning10aOntologies10aSemantic Web1 aKnorr, Matthias1 aAlferes, José, Júlio1 aHitzler, Pascal uhttp://dx.doi.org/10.1016/j.artint.2011.01.00701042nas a2200193 4500008004100000020002200041245008200063210006900145260002200214300001200236490000900248520043600257100002100693700002000714700002000734700002300754700002300777856004800800 2011 eng d a978-3-642-23579-500aLocal Closed World Semantics: Grounded Circumscription for Description Logics0 aLocal Closed World Semantics Grounded Circumscription for Descri bSpringerc08/2011 a263-2680 v69023 aWe present an improved local closed world extension for description logics. It is based on circumscription, and deviates from previous circumscriptive description logics in that extensions of minimized predicates may contain only extensions of named individuals in the knowledge base. Besides an (arguably) higher intuitive appeal, the improved semantics is applicable to expressive description logics without loss of decidability.1 aKrisnadhi, Adila1 aSengupta, Kunal1 aHitzler, Pascal1 aRudolph, Sebastian1 aGutierrez, Claudio uhttp://dx.doi.org/10.1007/978-3-642-23580-101464nas a2200253 4500008004100000245006900041210006600110260002200176300001200198490000900210520067800219100002000897700002100917700002000938700001600958700001700974700001800991700001801009700002301027700001801050700002101068700001901089856010201108 2011 eng d00aLocal Closed World Semantics: Grounded Circumscription for {OWL}0 aLocal Closed World Semantics Grounded Circumscription for OWL bSpringerc10/2011 a617-6320 v70313 aWe present a new approach to adding closed world reasoning to the Web Ontology Language OWL. It transcends previous work on circumscriptive description logics which had the drawback of yielding an undecidable logic unless severe restrictions were imposed. In particular, it was not possible, in general, to apply local closure to roles. In this paper, we provide a new approach, called grounded circumscription, which is applicable to SROIQ and other description logics around OWL without these restrictions. We show that the resulting language is decidable, and we derive an upper complexity bound. We also provide a decision procedure in the form of a tableaux algorithm.1 aSengupta, Kunal1 aKrisnadhi, Adila1 aHitzler, Pascal1 aAroyo, Lora1 aWelty, Chris1 aAlani, Harith1 aTaylor, Jamie1 aBernstein, Abraham1 aKagal, Lalana1 aNoy, Natasha, F.1 aBlomqvist, Eva uhttps://daselab.cs.ksu.edu/publications/local-closed-world-semantics-grounded-circumscription-owl01673nas a2200229 4500008004100000245005800041210005500099260002500154490000800179520100400187653002001191653001701211653001701228653002201245100002101267700002001288700002001308700002101328700002301349700002701372856004401399 2011 eng d00aLocal Closed World Semantics: Keep it simple, stupid!0 aLocal Closed World Semantics Keep it simple stupid bCEUR-WS.orgc07/20110 v7453 aA combination of open and closed-world reasoning (usually called local closed world reasoning) is a desirable capability of knowledge representation formalisms for Semantic Web applications. However, none of the proposals made to date for extending description logics with local closed world capabilities has had any significant impact on applications. We believe that one of the key reasons for this is that current proposals fail to provide approaches which are intuitively accessible for application developers and at the same time are applicable, as extensions, to expressive description logics such as SROIQ, which underlies the Web Ontology Language OWL. In this paper we propose a new approach which overcomes key limitations of other major proposals made to date. It is based on an adaptation of circumscriptive description logics which, in contrast to previously reported circumscription proposals, is applicable to SROIQ without rendering reasoning over the resulting language undecidable.10acircumscription10aclosed world10adecidability10aDescription Logic1 aKrisnadhi, Adila1 aSengupta, Kunal1 aHitzler, Pascal1 aRosati, Riccardo1 aRudolph, Sebastian1 aZakharyaschev, Michael uhttp://ceur-ws.org/Vol-745/paper_12.pdf00673nam a2200181 4500008004100000022002200041245006500063210006400128260004100192100002500233700002100258700002400279700001700303700002000320700002200340700002100362856010800383 2011 eng d a978-3-935025-85-000aLogik und Logikprogrammierung Band 2: Aufgaben und Lösungen0 aLogik und Logikprogrammierung Band 2 Aufgaben und Lösungen aHeidelberg, GermanybSynchron Verlag1 aHölldobler, Steffen1 aBader, Sebastian1 aFronhöfer, Bertram1 aHans, Ursula1 aHitzler, Pascal1 aKrötzsch, Markus1 aPietzsch, Tobias uhttps://daselab.cs.ksu.edu/publications/logik-und-logikprogrammierung-band-2-aufgaben-und-l%C3%B6sungen00315nas a2200097 4500008004100000245003300041210003300074260001800107100002300125856006900148 2011 eng d00aMapSSS Results for OAEI 20110 aMapSSS Results for OAEI 2011 aBonn, Germany1 aCheatham, Michelle uhttps://daselab.cs.ksu.edu/publications/mapsss-results-oaei-201101013nas a2200253 4500008004100000245020200041210006900243260004100312490000900353100002100362700002300383700001900406700001700425700002200442700001300464700002000477700002100497700002500518700001700543700002300560700002000583700002400603856013200627 2011 eng d00aOn the Move to Meaningful Internet Systems: OTM 2011. Confederated International Conferences, CoopIS, DOA-SVI, and ODBASE 2011, Hersonissos, Crete, Greece, October 17-21, 2011, Proceedings, Part II0 aMove to Meaningful Internet Systems OTM 2011 Confederated Intern aHersonissos, Crete, GreecebSpringer0 v70451 aMeersman, Robert1 aDillon, Tharam, S.1 aHerrero, Pilar1 aKumar, Akhil1 aReichert, Manfred1 aQing, Li1 aOoi, Beng, Chin1 aDamiani, Ernesto1 aSchmidt, Douglas, C.1 aWhite, Jules1 aHauswirth, Manfred1 aHitzler, Pascal1 aMohania, Mukesh, K. uhttps://daselab.cs.ksu.edu/publications/move-meaningful-internet-systems-otm-2011-confederated-international-conferences-coop-001012nas a2200253 4500008004100000245020100041210006900242260004100311490000900352100002100361700002300382700001900405700001700424700002200441700001300463700002000476700002100496700002500517700001700542700002300559700002000582700002400602856013200626 2011 eng d00aOn the Move to Meaningful Internet Systems: OTM 2011. Confederated International Conferences, CoopIS, DOA-SVI, and ODBASE 2011, Hersonissos, Crete, Greece, October 17-21, 2011, Proceedings, Part I0 aMove to Meaningful Internet Systems OTM 2011 Confederated Intern aHersonissos, Crete, GreecebSpringer0 v70441 aMeersman, Robert1 aDillon, Tharam, S.1 aHerrero, Pilar1 aKumar, Akhil1 aReichert, Manfred1 aQing, Li1 aOoi, Beng, Chin1 aDamiani, Ernesto1 aSchmidt, Douglas, C.1 aWhite, Jules1 aHauswirth, Manfred1 aHitzler, Pascal1 aMohania, Mukesh, K. uhttps://daselab.cs.ksu.edu/publications/move-meaningful-internet-systems-otm-2011-confederated-international-conferences-coopis01014nas a2200193 4500008004100000245006500041210006500106260002500171490000800196520041700204100002200621700002100643700002100664700002000685700002100705700002300726700002700749856004400776 2011 eng d00aNominal Schemas for Integrating Rules and Description Logics0 aNominal Schemas for Integrating Rules and Description Logics bCEUR-WS.orgc07/20110 v7453 aWe propose an extension of SROIQ with nominal schemas which can be used like “variable nominal concepts” within axioms. This feature allows us to express arbitrary DL-safe rules in description logic syntax. We show that adding nominal schemas to SROIQ does not increase its worst-case reasoning complexity, and we identify a family of tractable DLs SROELVn that allow for restricted use of nominal schemas.1 aKrötzsch, Markus1 aMaier, Frederick1 aKrisnadhi, Adila1 aHitzler, Pascal1 aRosati, Riccardo1 aRudolph, Sebastian1 aZakharyaschev, Michael uhttp://ceur-ws.org/Vol-745/paper_39.pdf01164nas a2200253 4500008004100000020002200041245002000063210001800083260002200101300001200123490000900135520050100144100002100645700002100666700002000687700001900707700002100726700002000747700002500767700001800792700002100810700003100831856004800862 2011 eng d a978-3-642-23031-800a{OWL} and Rules0 aOWL and Rules bSpringerc08/2011 a382-4150 v68483 aThe relationship between the Web Ontology Language OWL and rule-based formalisms has been the subject of many discussions and research investigations, some of them controversial. From the many attempts to reconcile the two paradigms, we present some of the newest developments. More precisely, we show which kind of rules can be modeled in the current version of OWL, and we show how OWL can be extended to incorporate rules. We finally give references to a large body of work on rules and OWL. 1 aKrisnadhi, Adila1 aMaier, Frederick1 aHitzler, Pascal1 aPolleres, Axel1 ad'Amato, Claudia1 aArenas, Marcelo1 aHandschuh, Siegfried1 aKroner, Paula1 aOssowski, Sascha1 aPatel-Schneider, Peter, F. uhttp://dx.doi.org/10.1007/978-3-642-23032-501542nas a2200181 4500008004100000245006100041210006100102260003000163300001300193490000900206520099500215100001801210700001601228700002001244700002301264700002301287856005001310 2011 eng d00aParaconsistent Semantics for Hybrid MKNF Knowledge Bases0 aParaconsistent Semantics for Hybrid MKNF Knowledge Bases aGalway, IrelandbSpringer a93–1070 v69023 aHybrid MKNF knowledge bases, originally based on the stable model semantics, is a mature method of combining rules and Description Logics (DLs). The well-founded semantics for such knowledge bases has been proposed subsequently for better efficiency of reasoning. However, integration of rules and DLs may give rise to inconsistencies, even if they are respectively consistent. Accordingly, reasoning systems based on the previous two semantics will break down. In this paper, we employ the four-valued logic proposed by Belnap, and present a paraconsistent semantics for Hybrid MKNF knowledge bases, which can detect inconsistencies and handle it effectively. Besides, we transform our proposed semantics to the stable model semantics via a linear transformation operator, which indicates that the data complexity in our paradigm is not higher than that of classical reasoning. Moreover, we provide a fixpoint algorithm for computing paraconsistent MKNF models.
1 aHuang, Shasha1 aLi, Qingguo1 aHitzler, Pascal1 aRudolph, Sebastian1 aGutierrez, Claudio uhttp://dx.doi.org/10.1007/978-3-642-23580-1_800646nas a2200145 4500008004100000022001400041245022200055210006900277260004500346490000800391100003000399700002000429700002000449856003100469 2011 eng d a1613-007300aProceedings of the Seventh International Workshop on Neural-Symbolic Learning and Reasoning, NeSy'11, at the 22nd International Joint Conference on Artificial Intelligence, IJCAI-11, Barcelona, Catalonia (Spain), 20110 aProceedings of the Seventh International Workshop on NeuralSymbo aBarcelona, Catalonia, SpainbCEUR-WS.org0 v7641 aGarcez, Artur, S. d'Avila1 aHitzler, Pascal1 aLamb, Luís, C. uhttp://ceur-ws.org/Vol-76401489nas a2200181 4500008004100000245006100041210006000102260004800162490000800210520085700218100002101075700003201096700002001128700002001148700002201168700002201190856009501212 2011 eng d00aRepresentation of Parsimonious Covering Theory in OWL-DL0 aRepresentation of Parsimonious Covering Theory in OWLDL aSan Francisco, California, USAbCEUR-WS.org0 v7963 aThe Web Ontology Language has not been designed for representing abductive inference, which is often required for applications such as medical disease diagnosis. As a consequence, existing OWL ontologies have limited ability to encode knowledge for such applications. In the last 150 years, many logic frameworks for the representation of abductive inference have been developed. Among these frameworks, Parsimonious Covering Theory (PCT) has achieved wide recognition. PCT is a formal model of diagnostic reasoning in which knowledge is represented as a network of causal associations, and whose goal is to account for observed symptoms with plausible explanatory hypotheses. In this paper, we argue that OWL does provide some of the expressivity required to approximate diagnostic reasoning, and outline a suitable encoding of PCT in OWL-DL.
1 aHenson, Cory, A.1 aThirunarayan, Krishnaprasad1 aSheth, Amit, P.1 aHitzler, Pascal1 aDumontier, Michel1 aCourtot, Mélanie uhttps://daselab.cs.ksu.edu/publications/representation-parsimonious-covering-theory-owl-dl00352nas a2200121 4500008004100000245004200041210004200083300001200125490000600137100002000143700002400163856004300187 2011 eng d00aSemantic Web surveys and applications0 aSemantic Web surveys and applications a65–660 v21 aHitzler, Pascal1 aJanowicz, Krzysztof uhttp://dx.doi.org/10.3233/SW-2011-004700336nas a2200121 4500008004100000245003500041210003500076300001000111490000600121100002000127700002400147856004300171 2011 eng d00aSemantic Web tools and systems0 aSemantic Web tools and systems a1–20 v21 aHitzler, Pascal1 aJanowicz, Krzysztof uhttp://dx.doi.org/10.3233/SW-2011-003500909nas a2200121 4500008004100000245006700041210006600108260001200174520045600186100002400642700002000666856010100686 2011 eng d00aWeb Reasoning and Rule Systems: Five Years into the Conference0 aWeb Reasoning and Rule Systems Five Years into the Conference c12/20113 aIn this note we retrospect on the five years of the Web Reasoning and Rule Systems conference series and discuss the rationale for the series in the context of the overall field of the Semantic Web, the activities of the Web Reasoning research community, and the development of standards for rule-based systems on the Web. At the end, we draw the reader’s attention to the next event in the series, which will take place in Vienna in September 2012.1 aCalimeri, Francesco1 aHitzler, Pascal uhttp://www.cs.nmsu.edu/ALP/2011/12/web-reasoning-and-rule-systems-five-years-into-the-conference00499nas a2200145 4500008004100000245008100041210006900122260003000191300001200221490000900233100002000242700002000262700002100282856005000303 2011 eng d00aWhat's Happening in Semantic Web - ... and What FCA Could Have to Do with It0 aWhats Happening in Semantic Web and What FCA Could Have to Do wi aNicosia, CyprusbSpringer a18–230 v66281 aHitzler, Pascal1 aValtchev, Petko1 aJäschke, Robert uhttp://dx.doi.org/10.1007/978-3-642-20514-9_200780nas a2200217 4500008004100000022001400041245007600055210006900131260008300200653002400283653002000307653003200327653001700359653003000376653002200406100002000428700002000448700002000468700002000488856005400508 2010 eng d a1862-440500a10302 Abstracts Collection - Learning paradigms in dynamic environments0 a10302 Abstracts Collection Learning paradigms in dynamic environ aDagstuhl, GermanybSchloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany10aAutonomous learning10aDynamic systems10aNeural-symbolic integration10aNeurobiology10aRecurrent neural networks10aSpeech processing1 aHammer, Barbara1 aHitzler, Pascal1 aMaass, Wolfgang1 aToussaint, Marc uhttp://drops.dagstuhl.de/opus/volltexte/2010/280401481nas a2200193 4500008004100000245006500041210006100106260008300167520082300250100002001073700002001093700002001113700002001133700002001153700002001173700002001193700002001213856005401233 2010 eng d00a10302 Summary – Learning paradigms in dynamic environments0 a10302 Summary Learning paradigms in dynamic environments aDagstuhl, GermanybSchloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany3 aThe seminar centered around problems which arise in the context of machine learning in dynamic environments. Particular emphasis was put on a couple of specific questions in this context: how to represent and abstract knowledge appropriately to shape the problem of learning in a partially unknown and complex environment and how to combine statistical inference and abstract symbolic representations; how to infer from few data and how to deal with non i.i.d. data, model revision and life-long learning; how to come up with efficient strategies to control realistic environments for which exploration is costly, the dimensionality is high and data are sparse; how to deal with very large settings; and how to apply these models in challenging application areas such as robotics, computer vision, or the web.
1 aHammer, Barbara1 aHitzler, Pascal1 aMaass, Wolfgang1 aToussaint, Marc1 aHammer, Barbara1 aHitzler, Pascal1 aMaass, Wolfgang1 aToussaint, Marc uhttp://drops.dagstuhl.de/opus/volltexte/2010/280201583nas a2200193 4500008004100000245004900041210004900090260002800139300001400167490000900181520100500190100002701195700001901222700002001241700002701261700002701288700002301315856005101338 2010 eng d00aApproximate Instance Retrieval on Ontologies0 aApproximate Instance Retrieval on Ontologies aBilbao, SpainbSpringer a503–5110 v62613 aWith the development of more expressive description logics (DLs) for the Web Ontology Language OWL the question arises how we can properly deal with the high computational complexity for effi- cient reasoning. In application cases that require scalable reasoning with expressive ontologies, non-standard reasoning solutions such as approximate reasoning are necessary to tackle the intractability of reasoning in expressive DLs. In this paper, we are concerned with the approximation of the reasoning task of instance retrieval on DL knowledge bases, trading correctness of retrieval results for gain of speed. We introduce our notion of an approximate concept extension and we provide implementations to compute an approximate answer for a concept query by a suitable mapping to efficient database operations. Furthermore, we report on experiments of our approach on instance retrieval with the Wine ontology and discuss first results in terms of error rate and speed-up.
1 aTserendorj, Tuvshintur1 aGrimm, Stephan1 aHitzler, Pascal1 aBringas, Pablo, Garcia1 aHameurlain, Abdelkader1 aQuirchmayr, Gerald uhttp://dx.doi.org/10.1007/978-3-642-15364-8_4301617nas a2200229 4500008004100000245008100041210006900122300001100191490000600202520090100208653001401109653002901123653003001152653002901182653002301211100001201234700001501246700001701261700002001278700001701298856007201315 2010 eng d00aComputational Complexity and Anytime Algorithm for Inconsistency Measurement0 aComputational Complexity and Anytime Algorithm for Inconsistency a3–210 v43 aMeasuring inconsistency degrees of inconsistent knowledge bases is an important problem as it provides context information for facilitating inconsistency handling. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. In this paper, we consider the computational aspects of inconsistency degrees of propositional knowledge bases under 4-valued semantics. We first give a complete analysis of the computational complexity of computing inconsistency degrees. As it turns out that computing the exact inconsistency degree is intractable, we then propose an anytime algorithm that provides tractable approximations of the inconsistency degree from above and below. We show that our algorithm satisfies some desirable properties and give experimental results of our implementation of the algorithm
10aalgorithm10acomputational complexity10ainconsistency measurement10aKnowledge representation10amulti-valued logic1 aMa, Yue1 aQi, Guilin1 aXiao, Guohui1 aHitzler, Pascal1 aLin, Zuoquan uhttp://www.ijsi.org/ch/reader/view_abstract.aspx?file_no=i41&flag=101837nas a2200205 4500008004100000245007000041210006900111300001400180490000700194520119100201653002301392653003201415653000801447653002501455653001701480653003201497100001801529700002001547856006401567 2010 eng d00aConcept learning in description logics using refinement operators0 aConcept learning in description logics using refinement operator a203–2500 v783 aWith the advent of the Semantic Web, description logics have become one of the most prominent paradigms for knowledge representation and reasoning. Progress in research and applications, however, is constrained by the lack of well-structured knowledge bases consisting of a sophisticated schema and instance data adhering to this schema. It is paramount that suitable automated methods for their acquisition, maintenance, and evolution will be developed. In this paper, we provide a learning algorithm based on refinement operators for the description logic ALCQ including support for concrete roles. We develop the algorithm from thorough theoretical foundations by identifying possible abstract property combinations which refinement operators for description logics can have. Using these investigations as a basis, we derive a practically useful complete and proper refinement operator. The operator is then cast into a learning algorithm and evaluated using our implementation DL-Learner. The results of the evaluation show that our approach is superior to other learning approaches on description logics, and is competitive with established ILP systems.
10adescription logics10aInductive logic programming10aOWL10arefinement operators10aSemantic Web10aStructured Machine Learning1 aLehmann, Jens1 aHitzler, Pascal uhttp://springerlink.metapress.com/content/c040n45u15qrnu44/01613nas a2200181 4500008004100000245008400041210006900125260003400194300001200228490000800240520097700248100001201225700002001237700002101257700001701278700001901295856011701314 2010 eng d00aDistance-based Measures of Inconsistency and Incoherency for Description Logics0 aDistancebased Measures of Inconsistency and Incoherency for Desc aWaterloo, CanadabCEUR-WS.org a475-4850 v5733 aInconsistency and incoherency are two sorts of erroneous information in a DL ontology which have been widely discussed in ontology-based applications. For example, they have been used to detect modeling errors during ontology construction. To provide more informative metrics which can tell the differences between inconsistent ontologies and between incoherent terminologies, there has been some work on measuring inconsistency of an ontology and on measuring incoherency of a terminology. However, most of them merely focus either on measuring inconsistency or on measuring incoherency and no clear ideas of how to extend them to allow for the other. In this paper, we propose a novel approach to measure DL ontologies, named distance-based measures. It has the merits that both inconsistency and incoherency can be measured in a unified framework. Moreover, only classical DL interpretations are used such that there is no restriction on the DL languages used.
1 aMa, Yue1 aHitzler, Pascal1 aHaarslev, Volker1 aToman, David1 aWeddell, Grant uhttps://daselab.cs.ksu.edu/publications/distance-based-measures-inconsistency-and-incoherency-description-logics01230nas a2200133 4500008004100000245005200041210005000093260004500143520075900188100002100947700002300968700002000991856008501011 2010 eng d00aDistributed Reasoning with EL++ Using MapReduce0 aDistributed Reasoning with EL Using MapReduce aDayton, OH, USAbWright State University3 aIt has recently been shown that the MapReduce framework for distributed computation can be used effectively for large-scale RDF Schema reasoning, computing the deductive closure of over a billion RDF triples within a reasonable time [23]. Later work has carried this approach over to OWL Horst [22]. In this paper, we provide a MapReduce algorithm for classifying knowledge bases in the description logic EL++, which is essentially the OWL 2 profile OWL 2 EL. The traditional EL++ classification algorithm is recast into a form compatible with MapReduce, and it is shown how the revised algorithm can be realized within the MapReduce framework. An analysis of the circumstances under which the algorithm can be effectively used is also provided.
1 aMaier, Frederick1 aMutharaju, Raghava1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/distributed-reasoning-el-using-mapreduce01300nas a2200145 4500008004100000245007000041210006900111300001400180490000700194520084600201100001801047700002101065700002001086856004801106 2010 eng d00aExtracting Reduced Logic Programs from Artificial Neural Networks0 aExtracting Reduced Logic Programs from Artificial Neural Network a249–2660 v323 aArtificial neural networks can be trained to perform excellently in many application areas. Whilst they can learn from raw data to solve sophisticated recognition and analysis problems, the acquired knowledge remains hidden within the network architecture and is not readily accessible for analysis or further use: Trained networks are black boxes. Recent research efforts therefore investigate the possibility to extract symbolic knowledge from trained networks, in order to analyze, validate, and reuse the structural insights gained implicitly during the training process. In this paper, we will study how knowledge in form of propositional logic programs can be obtained in such a way that the programs are as simple as possible — where simple is being understood in some clearly defined and meaningful way.
1 aLehmann, Jens1 aBader, Sebastian1 aHitzler, Pascal uhttp://dx.doi.org/10.1007/s10489-008-0142-y01253nas a2200217 4500008004100000245005200041210005100093260003300144490000800177520061500185100001800800700002000818700002000838700001900858700002200877700002400899700002700923700001400950700002100964856005000985 2010 eng d00aFlexible Bootstrapping-Based Ontology Alignment0 aFlexible BootstrappingBased Ontology Alignment aShanghai, ChinabCEUR-WS.org0 v6893 aBLOOMS (Jain et al, ISWC2010, to appear) is an ontology alignment system which, in its core, utilizes the Wikipedia category hierarchy for establishing alignments. In this paper, we present a Plug-and-Play extension to BLOOMS, which allows to flexibly replace or complement the use of Wikipedia by other online or offline resources, including domain-specific ontologies or taxonomies. By making use of automated translation services and of Wikipedia in languages other than English, it makes it possible to apply BLOOMS to alignment tasks where the input ontologies are written in different languages.
1 aJain, Prateek1 aHitzler, Pascal1 aSheth, Amit, P.1 aShvaiko, Pavel1 aEuzenat, Jérôme1 aGiunchiglia, Fausto1 aStuckenschmidt, Heiner1 aMao, Ming1 aCruz, Isabel, F. uhttp://ceur-ws.org/Vol-689/om2010_poster9.pdf01428nas a2200229 4500008004100000245006400041210006400105300001400169490000700183520074500190653002700935653002500962653003500987653002201022653001701044653002001061653001301081653001801094100002201112700002001134856004401154 2010 eng d00aGeneralized Distance Functions in the Theory of Computation0 aGeneralized Distance Functions in the Theory of Computation a443–4640 v533 aWe discuss a number of distance functions encountered in the theory of computation, including metrics, ultra-metrics, quasi-metrics, generalized ultra-metrics, partial metrics, d-ultra-metrics and generalized metrics. We consider their properties, associated fixed-point theorems and some general applications they have within the theory of computation. We consider in detail the applications of generalized distance functions in giving a uniform treatment of several important semantics for logic programs, including acceptable programs and natural generalizations of them, and also the supported model and the stable model in the context of locally stratified extended disjunctive logic programs and databases.
10adenotational semantics10afixed-point theorems10ageneralized distance functions10aLogic Programming10astable model10asupported model10atopology10aultra-metrics1 aSeda, Anthony, K.1 aHitzler, Pascal uhttp://dx.doi.org/10.1093/comjnl/bxm10801035nas a2200205 4500008004100000245003600041210003600077260003600113520044200149100001800591700002000609700001900629700001700648700001600665700001800681700002400699700001800723700002400741856006400765 2010 eng d00aLinked Data Is Merely More Data0 aLinked Data Is Merely More Data aStanford, California, USAbAAAI3 aIn this position paper, we argue that the Linked Open Data (LoD) Cloud, in its current form, is only of limited value for furthering the Semantic Web vision. Being merely a weakly linked “triple collection,” it will only be of very limited bene- fit for the AI or Semantic Web communities. We describe the corresponding problems with the LoD Cloud and give directions for research to remedy the situation.
1 aJain, Prateek1 aHitzler, Pascal1 aYeh, Peter, Z.1 aVerma, Kunal1 aSheth, Amit1 aBrickley, Dan1 aChaudhri, Vinay, K.1 aHalpin, Harry1 aMcGuinness, Deborah uhttp://www.aaai.org/ocs/index.php/SSS/SSS10/paper/view/113001195nas a2200193 4500008004100000245003400041210003100075260004300106300001200149490000800161520066300169100002300832700002100855700002000876700002100896700001700917700002300934856004400957 2010 eng d00aA MapReduce Algorithm for EL+0 aMapReduce Algorithm for EL aWaterloo, Ontario, CanadabCEUR-WS.org a464-4740 v5733 aRecently, the use of the MapReduce framework for distributed RDF Schema reasoning has shown that it is possible to compute the deductive closure of sets of over a billion RDF triples within a reasonable time span [22], and that it is also possible to carry the approach over to OWL Horst [21]. Following this lead, in this paper we provide a MapReduce algorithm for the description logic EL+, more precisely for the classification of EL+ ontologies. To do this, we first modify the algorithm usually used for EL+ classification. The modified algorithm can then be converted into a MapReduce algorithm along the same key ideas as used for RDF schema.
1 aMutharaju, Raghava1 aMaier, Frederick1 aHitzler, Pascal1 aHaarslev, Volker1 aToman, David1 aWeddell, Grant, E. uhttp://ceur-ws.org/Vol-573/paper_35.pdf00479nam a2200133 4500008004100000022001800041245005600059210005600115260003100171300000800202100002000210700002200230856009300252 2010 eng d a978143982961500aMathematical Aspects of Logic Programming Semantics0 aMathematical Aspects of Logic Programming Semantics bChapman and Hall/CRC Press a3041 aHitzler, Pascal1 aSeda, Anthony, K. uhttps://daselab.cs.ksu.edu/publications/mathematical-aspects-logic-programming-semantics00656nas a2200181 4500008004100000245008200041210006900123260003400192100001400226700001500240700001300255700002000268700002100288700001800309700001200327700001600339856011900355 2010 eng d00aNormalized MEDLINE Distance in Context-Aware Life Science Literature Searches0 aNormalized MEDLINE Distance in ContextAware Life Science Literat bTsinghua Science & Technology1 aWang, Yan1 aWang, Cong1 aZeng, Yi1 aHuang, Zhisheng1 aMomtchev, Vassil1 aAndersson, Bo1 aRen, Xu1 aZhong, Ning uhttps://daselab.cs.ksu.edu/publications/normalized-medline-distance-context-aware-life-science-literature-searches01773nas a2200277 4500008004100000245004400041210004400085260003000129300001400159490000900173520102400182100001801206700002001224700001601244700001701260700001901277700003101296700001301327700002001340700001601360700001501376700001801391700001801409700001701427856005101444 2010 eng d00aOntology Alignment for Linked Open Data0 aOntology Alignment for Linked Open Data aShanghai, ChinabSpringer a402–4170 v64963 aThe Web of Data currently coming into existence through the Linked Open Data (LOD) effort is a major milestone in realizing the Semantic Web vision. However, the development of applications based on LOD faces difficulties due to the fact that the different LOD datasets are rather loosely connected pieces of information. In particular, links between LOD datasets are almost exclusively on the level of instances, and schema-level information is being ignored. In this paper, we therefore present a system for finding schema-level links between LOD datasets in the sense of ontology alignment. Our system, called BLOOMS, is based on the idea of bootstrapping information already present on the LOD cloud. We also present a comprehensive evaluation which shows that BLOOMS outperforms state-of-the-art ontology alignment systems on LOD datasets. At the same time, BLOOMS is also competitive compared with these other systems on the Ontology Evaluation Alignment Initiative Benchmark datasets.
1 aJain, Prateek1 aHitzler, Pascal1 aSheth, Amit1 aVerma, Kunal1 aYeh, Peter, Z.1 aPatel-Schneider, Peter, F.1 aPan, Yue1 aHitzler, Pascal1 aMika, Peter1 aZhang, Lei1 aPan, Jeff, Z.1 aHorrocks, Ian1 aGlimm, Birte uhttp://dx.doi.org/10.1007/978-3-642-17746-0_2600466nas a2200145 4500008004100000245007000041210006900111300001400180490000700194100001600201700002000217700002000237700001900257856004400276 2010 eng d00aPerspectives and challenges for recurrent neural network training0 aPerspectives and challenges for recurrent neural network trainin a617–6190 v181 aGori, Marco1 aHammer, Barbara1 aHitzler, Pascal1 aPalm, Guenther uhttp://dx.doi.org/10.1093/jigpal/jzp04200474nas a2200145 4500008004100000245007400041210006900115300001000184490000700194100002400201700002000225700002000245700001500265856004800280 2010 eng d00aPreface - Special issue on commonsense reasoning for the semantic web0 aPreface Special issue on commonsense reasoning for the semantic a1–20 v581 avan Harmelen, Frank1 aHerzig, Andreas1 aHitzler, Pascal1 aQi, Guilin uhttp://dx.doi.org/10.1007/s10472-010-9209-702649nas a2200277 4500008004100000245009100041210006900132260003400201300001400235490000900249520174100258653003601999653001902035653003002054653003702084653002202121653002002143100002102163700002502184700002002209700001602229700003202245700001902277700002402296856005102320 2010 eng d00aProvenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data0 aProvenance Context Entity PaCE Scalable Provenance Tracking for aHeidelberg, GermanybSpringer a461–4700 v61873 aThe Semantic Web Resource Description Framework (RDF) format is being used by a large number of scientific applications to store and disseminate their datasets. The provenance information, describing the source or lineage of the datasets, is playing an increasingly significant role in ensuring data quality, computing trust value of the datasets, and ranking query results. Current Semantic Web provenance tracking approaches using the RDF reification vocabulary suffer from a number of known issues, including lack of formal semantics, use of blank nodes, and application-dependent interpretation of reified RDF triples that hinders data sharing. In this paper, we introduce a new approach called Provenance Context Entity (PaCE) that uses the notion of provenance context to create provenance-aware RDF triples without the use of RDF reification or blank nodes. We also define the formal semantics of PaCE through a simple extension of the existing RDF(S) semantics that ensures compatibility of PaCE with existing Semantic Web tools and implementations. We have implemented the PaCE approach in the Biomedical Knowledge Repository (BKR) project at the US National Library of Medicine to support provenance tracking on RDF data extracted from multiple sources, including biomedical literature and the UMLS Metathesaurus. The evaluations demonstrate a minimum of 49% reduction in total number of provenancespecific RDF triples generated using the PaCE approach as compared to RDF reification. In addition, using the PACE approach improves the performance of complex provenance queries by three orders of magnitude and remains comparable to the RDF reification approach for simpler provenance queries.
10aBiomedical knowledge repository10aContext theory10aProvenance context entity10aProvenance Management Framework.10aProvenir ontology10aRDF reification1 aSahoo, Satya, S.1 aBodenreider, Olivier1 aHitzler, Pascal1 aSheth, Amit1 aThirunarayan, Krishnaprasad1 aGertz, Michael1 aLudäscher, Bertram uhttp://dx.doi.org/10.1007/978-3-642-13818-8_3201540nas a2200193 4500008004100000245003000041210002800071300001200099490000600111520103000117653002401147653002101171653002901192653002101221653001701242100002001259700002401279856004301303 2010 eng d00aA Reasonable Semantic Web0 aReasonable Semantic Web a39–440 v13 aThe realization of Semantic Web reasoning is central to substantiating the Semantic Web vision. However, current mainstream research on this topic faces serious challenges, which forces us to question established lines of research and to rethink the underlying approaches. We argue that reasoning for the Semantic Web should be understood as "shared inference," which is not necessarily based on deductive methods. Model-theoretic semantics (and sound and complete reasoning based on it) functions as a gold standard, but applications dealing with large-scale and noisy data usually cannot afford the required runtimes. Approximate methods, including deductive ones, but also approaches based on entirely different methods like machine learning or natureinspired computing need to be investigated, while quality assurance needs to be done in terms of precision and recall values (as in information retrieval) and not necessarily in terms of soundness and completeness of the underlying algorithms.
10aAutomated Reasoning10aFormal Semantics10aKnowledge representation10aLinked Open Data10aSemantic Web1 aHitzler, Pascal1 avan Harmelen, Frank uhttp://dx.doi.org/10.3233/SW-2010-001001406nas a2200481 4500008004100000245005000041210005000091300001300141490000700154100001900161700002000180700001900200700003000219700002000249700002100269700002600290700002900316700002400345700002000369700002400389700002500413700002800438700002300466700002100489700002000510700002100530700002100551700001800572700002000590700001600610700001600626700002000642700002400662700002200686700002100708700002100729700001600750700002400766700002100790700002300811700002300834856006700857 2010 eng d00aReports of the AAAI 2010 Conference Workshops0 aReports of the AAAI 2010 Conference Workshops a95–1080 v311 aAha, David, W.1 aBoddy, Mark, S.1 aBulitko, Vadim1 aGarcez, Artur, S. d'Avila1 aDoshi, Prashant1 aEdelkamp, Stefan1 aGeib, Christopher, W.1 aGmytrasiewicz, Piotr, J.1 aGoldman, Robert, P.1 aHitzler, Pascal1 aIsbell, Charles, L.1 aJosyula, Darsana, P.1 aKaelbling, Leslie, Pack1 aKersting, Kristian1 aKunda, Maithilee1 aLamb, Luís, C.1 aMarthi, Bhaskara1 aMcGreggor, Keith1 aNastase, Vivi1 aProvan, Gregory1 aRaja, Anita1 aRam, Ashwin1 aRiedl, Mark, O.1 aRussell, Stuart, J.1 aSabharwal, Ashish1 aSmaus, Jan-Georg1 aSukthankar, Gita1 aTuyls, Karl1 avan der Meyden, Ron1 aHalevy, Alon, Y.1 aMihalkova, Lilyana1 aNatarajan, Sriraam uhttp://www.aaai.org/ojs/index.php/aimagazine/article/view/231800979nas a2200133 4500008004100000245006200041210005800103300001000161490000600171520058100177100002000758700002400778856004300802 2010 eng d00aSemantic Web - Interoperability, Usability, Applicability0 aSemantic Web Interoperability Usability Applicability a1–20 v13 aThe Semantic Web journal is set up to be a forum for highest-quality research contributions on all aspects of the Semantic Web. Its scope encompasses work in neighboring disciplines which is motivated by the Semantic Web vision. Besides the publishing of research contributions, it is also an outlet for reports on tools, systems, applications, and ontologies which enable research, rather than being direct research contributions. The journal also publishes top-quality surveys which serve as introductions to core topics of Semantic Web research.
1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttp://dx.doi.org/10.3233/SW-2010-001700777nas a2200193 4500008004100000245015800041210006900199260003000268490000900298100003100307700001300338700002000351700001600371700001500387700001800402700001800420700001700438856012800455 2010 eng d00aThe Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I0 aSemantic Web ISWC 2010 9th International Semantic Web Conference aShanghai, ChinabSpringer0 v64961 aPatel-Schneider, Peter, F.1 aPan, Yue1 aHitzler, Pascal1 aMika, Peter1 aZhang, Lei1 aPan, Jeff, Z.1 aHorrocks, Ian1 aGlimm, Birte uhttps://daselab.cs.ksu.edu/publications/semantic-web-iswc-2010-9th-international-semantic-web-conference-iswc-2010-shanghai00780nas a2200193 4500008004100000245015900041210006900200260003000269490000900299100003100308700001300339700002000352700001600372700001500388700001800403700001800421700001700439856013000456 2010 eng d00aThe Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part II0 aSemantic Web ISWC 2010 9th International Semantic Web Conference aShanghai, ChinabSpringer0 v64971 aPatel-Schneider, Peter, F.1 aPan, Yue1 aHitzler, Pascal1 aMika, Peter1 aZhang, Lei1 aPan, Jeff, Z.1 aHorrocks, Ian1 aGlimm, Birte uhttps://daselab.cs.ksu.edu/publications/semantic-web-iswc-2010-9th-international-semantic-web-conference-iswc-2010-shanghai-000522nas a2200169 4500008004100000245007200041210006900113300001400182100001200196700001300208700001500221700001600236700002000252700001400272700001500286856005100301 2010 eng d00aSocial Relation Based Search Refinement: Let Your Friends Help You!0 aSocial Relation Based Search Refinement Let Your Friends Help Yo a475–4851 aRen, Xu1 aZeng, Yi1 aQin, Yulin1 aZhong, Ning1 aHuang, Zhisheng1 aWang, Yan1 aWang, Cong uhttp://dx.doi.org/10.1007/978-3-642-15470-6_4800311nas a2200097 4500008004100000245003100041210003100072260001600103100002300119856007100142 2010 eng d00aTargeted Ontology Matching0 aTargeted Ontology Matching aChicago, IL1 aCheatham, Michelle uhttps://daselab.cs.ksu.edu/publications/targeted-ontology-matching00527nas a2200157 4500008004100000245009300041210006900134300001300203100001300216700001400229700002000243700002400263700001600287700001500303856005100318 2010 eng d00aUser Interests: Definition, Vocabulary, and Utilization in Unifying Search and Reasoning0 aUser Interests Definition Vocabulary and Utilization in Unifying a98–1071 aZeng, Yi1 aWang, Yan1 aHuang, Zhisheng1 aDamljanovic, Danica1 aZhong, Ning1 aWang, Cong uhttp://dx.doi.org/10.1007/978-3-642-15470-6_1100581nas a2200121 4500008004100000245013200041210006900173260003200242490000900274100002000283700002400303856013200327 2010 eng d00aWeb Reasoning and Rule Systems. Fourth International Conference, RR 2010, Bressanone, Italy, September 22-24, 2010, Proceedings0 aWeb Reasoning and Rule Systems Fourth International Conference R aBressanone, ItalybSpringer0 v63331 aHitzler, Pascal1 aLukasiewicz, Thomas uhttps://daselab.cs.ksu.edu/publications/web-reasoning-and-rule-systems-fourth-international-conference-rr-2010-bressanone-italy01441nas a2200205 4500008004100000245006500041210006200106260003000168300001200198490000900210520084600219100001201065700001501077700001701092700002001109700001701129700002601146700001301172856005001185 2009 eng d00aAn Anytime Algorithm for Computing Inconsistency Measurement0 aAnytime Algorithm for Computing Inconsistency Measurement aVienna, AustriabSpringer a29–400 v59143 aMeasuring inconsistency degrees of inconsistent knowledge bases is an important problem as it provides context information for facilitating inconsistency handling. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. In this paper, we consider the computational aspects of inconsistency degrees of propositional knowledge bases under 4-valued semantics. We first analyze its computational complexity. As it turns out that computing the exact inconsistency degree is intractable, we then propose an anytime algorithm that provides tractable approximation of the inconsistency degree from above and below. We show that our algorithm satisfies some desirable properties and give experimental results of our implementation of the algorithm.
1 aMa, Yue1 aQi, Guilin1 aXiao, Guohui1 aHitzler, Pascal1 aLin, Zuoquan1 aKaragiannis, Dimitris1 aJin, Zhi uhttp://dx.doi.org/10.1007/978-3-642-10488-6_700654nas a2200133 4500008004100000022002200041245015600063210006900219260004500288100001800333700002000351700001900371856013000390 2009 eng d a978-90-78677-24-600aArtificial General Intelligence. Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009. Proceedings0 aArtificial General Intelligence Second Conference on Artificial aArlington, Virginia, USAbAtlantis Press1 aGoertzel, Ben1 aHitzler, Pascal1 aHutter, Marcus uhttps://daselab.cs.ksu.edu/publications/artificial-general-intelligence-second-conference-artificial-general-intelligence-agi00418nam a2200133 4500008004100000022001800041245003800059210003800097260002500135300000800160100002000168700002100188856007500209 2009 eng d a978142006062100aConceptual Structures in Practice0 aConceptual Structures in Practice bChapman and Hall/CRC a4251 aHitzler, Pascal1 aSchärfe, Henrik uhttps://daselab.cs.ksu.edu/publications/conceptual-structures-practice00562nas a2200145 4500008004100000245007300041210006900114260002800183100001800211700002300229700002200252700002300274700002000297856009900317 2009 eng d00aAn Evolutionary Computing Approach for Reasoning in the Semantic Web0 aEvolutionary Computing Approach for Reasoning in the Semantic We aLeiden, The Netherlands1 aTagni, Gaston1 aGueret, Christophe1 aSchlobach, Stefan1 aRudolph, Sebastian1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/evolutionary-computing-approach-reasoning-semantic-web00913nas a2200133 4500008004100000245004600041210004600087300001200133490000700145520047200152100002000624700002500644856011000669 2009 eng d00aFacets of Artificial General Intelligence0 aFacets of Artificial General Intelligence a58–590 v233 aWe argue that time has come for a serious endeavor to work towards artificial general intelligence (AGI). This positive assessment of the very possibility of AGI has partially its roots in the development of new methodological achievements in the AI area, like new learning paradigms and new integration techniques for different methodologies. The article sketches some of these methods as prototypical examples for approaches towards AGI.
1 aHitzler, Pascal1 aKühnberger, Kai-Uwe uhttp://www.kuenstliche-intelligenz.de/fileadmin/template/main/archiv/pdf/ki2009-02_page58-59_web_full.pdf00481nam a2200145 4500008004100000022001800041245004500059210004500104260003100149300000800180100002000188700002200208700002300230856008200253 2009 eng d a978142009050500aFoundations of Semantic Web Technologies0 aFoundations of Semantic Web Technologies bChapman and Hall/CRC Press a4551 aHitzler, Pascal1 aKrötzsch, Markus1 aRudolph, Sebastian uhttps://daselab.cs.ksu.edu/publications/foundations-semantic-web-technologies00740nas a2200169 4500008004100000245006500041210005600106260002900162300001200191520016300203100002000366700002500386700001800411700002000429700001900449856010200468 2009 eng d00aThe Importance of Being Neural-Symbolic – A Wilde Position0 aImportance of Being NeuralSymbolic A Wilde Position aArlington, Virginia, USA a208-2093 aWe argue that Neural-Symbolic Integration is a topic of central importance for the advancement of Artificial General Intelligence.
1 aHitzler, Pascal1 aKühnberger, Kai-Uwe1 aGoertzel, Ben1 aHitzler, Pascal1 aHutter, Marcus uhttps://daselab.cs.ksu.edu/publications/importance-being-neural-symbolic-%E2%80%93-wilde-position00399nas a2200121 4500008004100000245003900041210003700080300000700117100003200124700002000156700002400176856007700200 2009 eng d00aKI 2009 - AI Mashup Challenge 20090 aKI 2009 AI Mashup Challenge 2009 a521 aEndres-Niggemeyer, Brigitte1 aHitzler, Pascal1 aZacharias, Valentin uhttps://daselab.cs.ksu.edu/publications/ki-2009-ai-mashup-challenge-200900419nas a2200157 4500008004100000245002500041210002500066250000600091260001300097300001200110100002000122700001800142700001900160700001700179856006500196 2009 eng d00aOntologies and Rules0 aOntologies and Rules a2 bSpringer a111-1321 aHitzler, Pascal1 aParsia, Bijan1 aStaab, Steffen1 aStuder, Rudi uhttps://daselab.cs.ksu.edu/publications/ontologies-and-rules00588nas a2200169 4500008004100000245005900041210005900100260002300159100002300182700002100205700002100226700002300247700001700270700001600287700001900303856009600322 2009 eng d00aOntology Driven Integration of Biology Experiment Data0 aOntology Driven Integration of Biology Experiment Data aCleveland, OH, USA1 aMutharaju, Raghava1 aSahoo, Satya, S.1 aWeatherly, Brent1 aAnantharam, Pramod1 aLogan, Flora1 aSheth, Amit1 aTarleton, Rick uhttps://daselab.cs.ksu.edu/publications/ontology-driven-integration-biology-experiment-data02427nas a2200229 4500008004100000245009100041210006900132260003400201300001500235490000900250520170100259100002101960700002101981700002302002700002302025700001602048700001902064700002102083700002302104700001902127856005102146 2009 eng d00aOntology-Driven Provenance Management in eScience: An Application in Parasite Research0 aOntologyDriven Provenance Management in eScience An Application aVilamoura, PortugalbSpringer a992–10090 v58713 aProvenance, from the French word “provenir”, describes the lineage or history of a data entity. Provenance is critical information in scientific applications to verify experiment process, validate data quality and associate trust values with scientific results. Current industrial scale eScience projects require an end-to-end provenance management infrastructure. This infrastructure needs to be underpinned by formal semantics to enable analysis of large scale provenance information by software applications. Further, effective analysis of provenance information requires well-defined query mechanisms to support complex queries over large datasets. This paper introduces an ontology-driven provenance management infrastructure for biology experiment data, as part of the Semantic Problem Solving Environment (SPSE) for Trypanosoma cruzi (T.cruzi). This provenance infrastructure, called T.cruzi Provenance Management System (PMS), is underpinned by (a) a domain-specific provenance ontology called Parasite Experiment ontology, (b) specialized query operators for provenance analysis, and (c) a provenance query engine. The query engine uses a novel optimization technique based on materialized views called materialized provenance views (MPV) to scale with increasing data size and query complexity. This comprehensive ontology-driven provenance infrastructure not only allows effective tracking and management of ongoing experiments in the Tarleton Research Group at the Center for Tropical and Emerging Global Diseases (CTEGD), but also enables researchers to retrieve the complete provenance information of scientific results for publication in literature.
1 aSahoo, Satya, S.1 aWeatherly, Brent1 aMutharaju, Raghava1 aAnantharam, Pramod1 aSheth, Amit1 aTarleton, Rick1 aMeersman, Robert1 aDillon, Tharam, S.1 aHerrero, Pilar uhttp://dx.doi.org/10.1007/978-3-642-05151-7_1800485nas a2200157 4500008004100000245004000041210003900081260001500120300002300135100002000158700002200178700001800200700003100218700002300249856005500272 2009 eng d00aOWL 2 Web Ontology Language: Primer0 aOWL 2 Web Ontology Language Primer c10/27/2009 aW3C Recommendation1 aHitzler, Pascal1 aKrötzsch, Markus1 aParsia, Bijan1 aPatel-Schneider, Peter, F.1 aRudolph, Sebastian uhttp://www.w3.org/TR/2009/REC-owl2-primer-2009102701311nas a2200169 4500008004100000245003900041210003900080260003300119300001400152490000900166520084400175100001201019700002001031700001901051700002001070856005101090 2009 eng d00aParaconsistent Reasoning for OWL 20 aParaconsistent Reasoning for OWL 2 aChantilly, VA, USAbSpringer a197–2110 v58373 aA four-valued description logic has been proposed to reason with description logic based inconsistent knowledge bases. This approach has a distinct advantage that it can be implemented by invoking classical reasoners to keep the same complexity as under the classical semantics. However, this approach has so far only been studied for the basid description logic ALC. In this paper, we further study how to extend the four-valued semantics to the more expressive description logic SROIQ which underlies the forthcoming revision of the Web Ontology Language, OWL 2, and also investigate how it fares when adapated to tractable description logics including EL++, DL-Lite, and Horn-DLs. We define the four-valued semantics along the same lines as for ALC and show that we can retain most of the desired properties.
1 aMa, Yue1 aHitzler, Pascal1 aPolleres, Axel1 aSwift, Terrance uhttp://dx.doi.org/10.1007/978-3-642-05082-4_1401436nas a2200169 4500008004100000245006200041210006000103260003300163300001200196490000900208520092100217100001901138700002001157700001901177700002001196856005001216 2009 eng d00aA Preferential Tableaux Calculus for Circumscriptive ALCO0 aPreferential Tableaux Calculus for Circumscriptive ALCO aChantilly, VA, USAbSpringer a40–540 v58373 aNonmonotonic extensions of description logics (DLs) allow for default and local closed-world reasoning and are an acknowledged desired feature for applications, e.g. in the Semantic Web. A recent approach to such an extension is based on McCarthy’s circumscription, which rests on the principle of minimising the extension of selected predicates to close off dedicated parts of a domain model. While decidability and complexity results have been established in the literature, no practical algorithmisation for circumscriptive DLs has been proposed so far. In this paper, we present a tableaux calculus that can be used as a decision procedure for concept satisfiability with respect to conceptcircumscribed ALCO knowledge bases. The calculus builds on existing tableaux for classical DLs, extended by the notion of a preference clash to detect the non-minimality of constructed models.
1 aGrimm, Stephan1 aHitzler, Pascal1 aPolleres, Axel1 aSwift, Terrance uhttp://dx.doi.org/10.1007/978-3-642-05082-4_400592nas a2200133 4500008004100000022001400041245020700055210006900262260003800331490000800369100003000377700002000407856003100427 2009 eng d a1613-007300aProceedings of the Fifth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy'09, at the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, July 20090 aProceedings of the Fifth International Workshop on NeuralSymboli aPasadena, CaliforniabCEUR-WS.org0 v4811 aGarcez, Artur, S. d'Avila1 aHitzler, Pascal uhttp://ceur-ws.org/Vol-48100644nas a2200133 4500008004100000022002200041245015400063210006900217260004400286300001200330100001900342700002000361856012900381 2009 eng d a978-3-88579-248-200aProceedings of the Fourth International Workshop on Applications of Semantic Technologies, AST2009, at Informatik2009, Lübeck, Germany, October 20090 aProceedings of the Fourth International Workshop on Applications aLübeck, GermanybBonner Köllen Verlag a381-4001 aGrimm, Stephan1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/proceedings-fourth-international-workshop-applications-semantic-technologies-ast200901371nas a2200301 4500008004100000245005400041210005200095260003900147300001400186490000900200520052000209100001200729700001700741700001500758700002000773700002600793700001600819700002000835700002100855700002100876700001500897700001900912700002400931700001500955700001700970700003100987856005101018 2009 eng d00aRaDON - Repair and Diagnosis in Ontology Networks0 aRaDON Repair and Diagnosis in Ontology Networks aHeraklion, Crete, GreecebSpringer a863–8670 v55543 aOne of the major challenges in managing networked and dynamic ontologies is to handle inconsistencies in single ontologies, and inconsistencies introduced by integrating multiple distributed ontologies. Our RaDON system provides functionalities to repair and diagnose ontology networks by extending the capabilities of existing reasoners. The system integrates several new debugging and repairing algorithms, such as a relevance-directed algorithm to meet the various needs of the users.
1 aJi, Qiu1 aHaase, Peter1 aQi, Guilin1 aHitzler, Pascal1 aStadtmüller, Steffen1 aAroyo, Lora1 aTraverso, Paolo1 aCiravegna, Fabio1 aCimiano, Philipp1 aHeath, Tom1 aHyvönen, Eero1 aMizoguchi, Riichiro1 aOren, Eyal1 aSabou, Marta1 aSimperl, Elena, Paslaru Bo uhttp://dx.doi.org/10.1007/978-3-642-02121-3_7101251nas a2200229 4500008004100000245008900041210006900130260002900199300001400228490000900242520052600251100002500777700002100802700001600823700002000839700002300859700002100882700002200903700002500925700002000950856005100970 2009 eng d00aSpatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences0 aSpatioTemporalThematic Analysis of Citizen Sensor Data Challenge aPoznan, PolandbSpringer a539–5530 v58023 aWe present work in the spatio-temporal-thematic analysis of citizen-sensor observations pertaining to real-world events. Using Twitter as a platform for obtaining crowd-sourced observations, we explore the interplay between these 3 dimensions in extracting insightful summaries of social perceptions behind events. We present our experiences in building a web mashup application, Twitris [1] that extracts and facilitates the spatio-temporal-thematic exploration of event descriptor summaries.
1 aNagarajan, Meenakshi1 aGomadam, Karthik1 aSheth, Amit1 aRanabahu, Ajith1 aMutharaju, Raghava1 aJadhav, Ashutosh1 aVossen, Gottfried1 aLong, Darrell, D. E.1 aYu, Jeffrey, Xu uhttp://dx.doi.org/10.1007/978-3-642-04409-0_5200791nas a2200145 4500008004100000245002600041210002600067260004600093490000800139520036900147100002000516700002000536700003100556856005800587 2009 eng d00aSuggestions for OWL 30 aSuggestions for OWL 3 aChantilly, VA, United StatesbCEUR-WS.org0 v5293 aWith OWL 2 about to be completed, it is the right time to start discussions on possible future modifications of OWL. We present here a number of suggestions in order to discuss them with the OWL user community. They encompass expressive extensions on polynomial OWL 2 profiles, a suggestion for an OWL Rules language, and expressive extensions for OWL DL.
1 aHitzler, Pascal1 aHoekstra, Rinke1 aPatel-Schneider, Peter, F. uhttp://ceur-ws.org/Vol-529/owled2009_submission_6.pdf00770nas a2200169 4500008004100000245003300041210003300074260003400107300001100141490000900152520030400161100002000465700002400485700001900509700002200528856005000550 2009 eng d00aTowards Reasoning Pragmatics0 aTowards Reasoning Pragmatics aMexico City, MexicobSpringer a9–250 v58923 aThe realization of Semantic Web reasoning is central to substantiating the Semantic Web vision. However, current mainstream research on this topic faces serious challenges, which force us to question established lines of research and to rethink the underlying approaches.
1 aHitzler, Pascal1 aJanowicz, Krzysztof1 aRaubal, Martin1 aLevashkin, Sergei uhttp://dx.doi.org/10.1007/978-3-642-10436-7_200638nas a2200169 4500008004100000245007800041210006900119260002300188100002300211700002100234700002100255700001700276700002300293700001600316700001900332856011700351 2009 eng d00aTrykipedia: Collaborative Bio-Ontology Development using Wiki Environment0 aTrykipedia Collaborative BioOntology Development using Wiki Envi aCleveland, OH, USA1 aAnantharam, Pramod1 aSahoo, Satya, S.1 aWeatherly, Brent1 aLogan, Flora1 aMutharaju, Raghava1 aSheth, Amit1 aTarleton, Rick uhttps://daselab.cs.ksu.edu/publications/trykipedia-collaborative-bio-ontology-development-using-wiki-environment01167nas a2200205 4500008004100000245004200041210004100083260002300124520055100147100002100698700001600719700002300735700002300758700001700781700001600798700002100814700002500835700002000860856008100880 2009 eng d00aTwitris: Socially Influenced Browsing0 aTwitris Socially Influenced Browsing aWashington DC, USA3 aIn this paper, we present Twitris, a semantic Web application that facilitates browsing for news and information, using social perceptions as the fulcrum. In doing so we address challenges in large scale crawling, processing of real time information, and preserving spatiotemporal-thematic properties central to observations pertaining to realtime events. We extract metadata about events from Twitter and bring related news and Wikipedia articles to the user. In developing Twitris, we have used the DBPedia ontology.
1 aJadhav, Ashutosh1 aWang, Wenbo1 aMutharaju, Raghava1 aAnantharam, Pramod1 aNguyen, Vinh1 aSheth, Amit1 aGomadam, Karthik1 aNagarajan, Meenakshi1 aRanabahu, Ajith uhttps://daselab.cs.ksu.edu/publications/twitris-socially-influenced-browsing00455nam a2200157 4500008004100000022002200041245002800063210002800091260002200119300000800141100002000149700002200169700002300191700001500214856006800229 2008 eng d a978-3-540-33993-900aSemantic Web Grundlagen0 aSemantic Web Grundlagen bSpringer textbook a2771 aHitzler, Pascal1 aKrötzsch, Markus1 aRudolph, Sebastian1 aSure, York uhttps://daselab.cs.ksu.edu/publications/semantic-web-grundlagen01068nas a2200169 4500008004100000245006100041210005900102260002200161300001200183490000900195520056000204100002100764700001800785700002300803700002100826856005100847 2007 eng d00aData Complexity in the {EL} Family of Description Logics0 aData Complexity in the EL Family of Description Logics bSpringerc10/2007 a333-3470 v47903 aWe study the data complexity of instance checking and conjunctive query answering in the EL family of description logics, with a particular emphasis on the boundary of tractability. We identify a large number of intractable extensions of EL, but also show that in ELIf , the extension of EL with inverse roles and global functionality, conjunctive query answering is tractable regarding data complexity. In contrast, already instance checking in EL extended with only inverse roles or global functionality is EXPTIME-complete regarding combined complexity1 aKrisnadhi, Adila1 aLutz, Carsten1 aDershowitz, Nachum1 aVoronkov, Andrei uhttp://dx.doi.org/10.1007/978-3-540-75560-9_2500598nas a2200205 4500008004100000245004600041210004400087260002500131490000800156100002100164700001800185700002100203700002100224700002100245700002000266700001700286700002400303700002100327856004400348 2007 eng d00aData Complexity in the {EL} family of DLs0 aData Complexity in the EL family of DLs bCEUR-WS.orgc06/20070 v2501 aKrisnadhi, Adila1 aLutz, Carsten1 aCalvanese, Diego1 aFranconi, Enrico1 aHaarslev, Volker1 aLembo, Domenico1 aMotik, Boris1 aTurhan, Anni-Yasmin1 aTessaris, Sergio uhttp://ceur-ws.org/Vol-250/paper_15.pdf03547nas a2200133 4500008004100000245008200041210006900123260005400192300000900246490002200255520305700277100002103334856005803355 2007 eng d00aData Complexity of Instance Checking in the {EL} Family of Description Logics0 aData Complexity of Instance Checking in the EL Family of Descrip aDresdenbTechnische Universität Dresdenc03/2007 av+680 vMaster of Science3 aSubsumption in the description logic (DL) EL is known to be tractable even when it is done with respect to the most general form of terminology, namely a set of general inclusion axioms (GCIs). Recently, this tractability boundary has been clarified by identifying DL constructors that causes intractability of subsumption when added to EL and that do not. These results provide us with a characterization of the complexity of subsumption for the EL family of DLs (i.e., EL and its extensions). Besides subsumption, there are other standard reasoning problems studied in DL. Among them, the instance checking problem is the most basic reasoning problem that is concerned with deriving implicit knowledge about individuals in a DL knowledge base. Such a knowledge base consists of an intensional part in the form of a terminology (TBox) and an extensional or data part in the form of assertions about particular individuals in the domain of the knowledge base (ABox). Like other reasoning problems, complexity of instance checking is usually measured in the size of the whole input - thus called combined complexity - which, in this case, consists of a TBox, an ABox, a query concept and an individual name. On the other hand, it is common to assume that the data (ABox) is very large compared to the TBox and the query. Therefore, it is often more realistic to use a complexity measure based only on the size of the ABox, i.e., data complexity. For the EL family, results for the combined complexity of instance checking can be derived from the complexity results for subsumption. But results which are concerned with data complexity are still lacking. This motivates us to investigate the data complexity of instance checking in the EL family. In particular, we are interested in whether there are extensions of EL which are intractable regarding combined complexity, but tractable regarding data complexity. The first part of this thesis establishes coNP-hardness (and even coNP-completeness) results regarding data complexity of instance checking w.r.t. sets of GCIs for extensions of EL with negation, disjunction, value restriction, number restriction and role constructors such as role negation, role union and transitive closures. The lower bounds of data complexity for these DLs are proved by polynomial reductions from the complement of 2+2-SAT, a variant of propositional satisfiability problem which is NP-complete, whereas the upper bounds follow from known results of data complexity for ALC and SHIQ. The second part identifies an extension of EL called ELIf, for which data complexity of instance checking w.r.t. sets of GCIs is tractable. The DL ELIf is obtained from EL by adding inverse roles and global functionality. This result is interesting since adding only one of those two constructors leads to intractability of reasoning w.r.t. combined complexity. The result is derived by giving an algorithm that decides instance checking in ELIf w.r.t. sets of GCIs and runs in time polynomial in the size of the input ABox.1 aKrisnadhi, Adila uhttp://lat.inf.tu-dresden.de/research/mas/#Kri-Mas-0700512nas a2200121 4500008004100000245008300041210006900124260001800193100002300211700002000234700002100254856011500275 2007 eng d00aFeature Selection for Collaborative Team Formation via Social Network Analysis0 aFeature Selection for Collaborative Team Formation via Social Ne aLas Vegas, NV1 aCheatham, Michelle1 aHarlow, Felicia1 aCleereman, Kevin uhttps://daselab.cs.ksu.edu/publications/feature-selection-collaborative-team-formation-social-network-analysis00466nas a2200109 4500008004100000245007500041210006900116260001800185100002300203700002100226856010900247 2006 eng d00aApplication of Social Network Analysis to Collaborative Team Formation0 aApplication of Social Network Analysis to Collaborative Team For aLas Vegas, NV1 aCheatham, Michelle1 aCleereman, Kevin uhttps://daselab.cs.ksu.edu/publications/application-social-network-analysis-collaborative-team-formation00490nas a2200109 4500008004100000245008900041210006900130260001800199100002300217700001800240856012200258 2006 eng d00aFeature and Prototype Evolution for Nearest Neighbor Classification of Web Documents0 aFeature and Prototype Evolution for Nearest Neighbor Classificat aLas Vegas, NV1 aCheatham, Michelle1 aRizki, Mateen uhttps://daselab.cs.ksu.edu/publications/feature-and-prototype-evolution-nearest-neighbor-classification-web-documents00507nas a2200109 4500008004100000245006800041210006700109260007700176100002300253700002600276856009500302 2006 eng d00aTraceability from Use Case to .NET Assembly via Design Patterns0 aTraceability from Use Case to NET Assembly via Design Patterns aAllahabad, IndiabMotilal Nehru National Institute of Technology (MNNIT)1 aMutharaju, Raghava1 aChaudhary, Banshi, D. uhttps://daselab.cs.ksu.edu/publications/traceability-use-case-net-assembly-design-patterns00423nas a2200109 4500008004100000245006000041210005900101260001600160100002300176700001700199856009700216 2005 eng d00aAI Planning in Portal-based Workflow Management Systems0 aAI Planning in Portalbased Workflow Management Systems aWaltham, MA1 aCheatham, Michelle1 aCox, Michael uhttps://daselab.cs.ksu.edu/publications/ai-planning-portal-based-workflow-management-systems00418nas a2200109 4500008004100000245005800041210005800099260001800157100002300175700001700198856009300215 2005 eng d00aAI Workflow Management in a Collaborative Environment0 aAI Workflow Management in a Collaborative Environment aSt. Louis, MO1 aCheatham, Michelle1 aCox, Michael uhttps://daselab.cs.ksu.edu/publications/ai-workflow-management-collaborative-environment