01891nas a2200193 4500008004100000245005900041210005900100260002200159300001400181520124700195653003001442653002501472653001901497653002701516100001701543700002101560700002001581856009601601 2025 eng d00aHidden Neuron Activation Analysis on Labeled Text Data0 aHidden Neuron Activation Analysis on Labeled Text Data aUSAbACMc12/2025 a206 - 2103 a

Understanding the internal mechanisms of deep neural networks remains a central challenge in the field of Explainable Artificial Intelligence (XAI). With the rapid advancement of neural architectures in natural language processing (NLP), analyzing the role of hidden neurons in capturing and processing linguistic features has become increasingly important. This study investigates Hidden Neuron Activation Analysis on labeled text data to reveal how individual neurons contribute to a model’s decision-making process. We propose a model-agnostic explainability framework for text classifiers that identifies concepts activating specific neurons involved in classification. An LSTM-based network is trained on the AG News topic classification dataset, comprising four distinct classes, and the final Dense layer with 64 neurons was analyzed. In addition, statistical analyses like the Mann-Whitney U Test is conducted to assess the robustness and reliability of the system. The statistical analysis shows that, concepts plays important role in the decision making process of neural network. Our findings enhance interpretability in NLP models and offer a foundation for optimizing neural architectures in text classification tasks.

10aConcept-based Explanation10aDense Layer Analysis10aExplainable AI10aHidden Neuron Analysis1 aDas, Avishek1 aDalal, Abhilekha1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/hidden-neuron-activation-analysis-labeled-text-data01724nas a2200181 4500008004100000245006500041210006500106520107500171653002101246653001801267653004001285653002001325653001701345100003401362700002101396700002001417856010501437 2024 eng d00aAutomating CNN Neuron Interpretation using Concept Induction0 aAutomating CNN Neuron Interpretation using Concept Induction3 a

This paper presents an automation pipeline for interpreting hidden neuron activations in Convolutional Neural Networks (CNNs), a crucial objective of Explainable AI (XAI). Previously, our research group addressed this objective by employing concept induction and semantic reasoning using a concept hierarchy derived from the Wikipedia knowledge graph. However, the process was executed manually, taking several days to complete. In this study, we have fully automated the workflow, achieving consistent results while significantly reducing the execution time. The automation pipeline streamlines model training, data preparation, concept induction, image retrieval, classification, and statistical validation, thereby completely eliminating the manual intervention. This automation enables us to efficiently interpret and validate CNN neuron activations by modifying parameters, such as incorporating a broader range of training images and classes and examining additional concept induction results across various neuron layers using different analytical tools.

10aAutomation in AI10aDeep Learning10aExplainable Artificial Intelligence10aKnowledge Graph10aSemantic Web1 aAkkamahadevi, Samatha, Ereshi1 aDalal, Abhilekha1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/automating-cnn-neuron-interpretation-using-concept-induction01414nas 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 a

We 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.pdf01713nas a2200313 4500008004100000245006300041210005800104260001200162490000700174520076200181653002100943653002300964653003100987653002101018653002901039100001901068700002001087700001601107700002001123700003001143700002101173700002301194700002201217700001701239700001901256700001601275700001701291856009101308 2020 eng d00aThe Enslaved Ontology: Peoples of the Historic Slave Trade0 aEnslaved Ontology Peoples of the Historic Slave Trade c08/20200 v633 a

We 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-trade01534nas 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 a

Interest 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-ontologies00883nas 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\_401559nas a2200193 4500008004100000245004600041210004600087260003300133520095200166653001101118653002601129653002801155653001101183100002301194700002001217700002201237700002001259856008601279 2015 eng d00aDistributed and Scalable OWL EL Reasoning0 aDistributed and Scalable OWL EL Reasoning aPortoroz, SloveniabSpringer3 a

OWL 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-reasoning01097nas 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.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 a

Automated 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 a

The 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_3601073nas a2200193 4500008004100000245005900041210005900100300001400159520049700173653002300670653000800693653002400701100001800725700002000743700002700763700002000790700001800810856005100828 2014 eng d00aPushing the Boundaries of Tractable Ontology Reasoning0 aPushing the Boundaries of Tractable Ontology Reasoning a148–1633 a

We 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_1001531nas 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-ontologies01419nas 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 a

We 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 a

Linked 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.2013.20601631nas 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/10.1145/2422085.242208701783nas 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 a

We 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.pdf02037nas 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-2012-006601470nas 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/jzs04301105nas 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 a

Extending 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_601166nas a2200157 4500008004100000245003800041210003800079300001400117520074700131653002300878653000800901653001000909100001800919700002000937856005100957 2012 eng d00aExtending Description Logic Rules0 aExtending Description Logic Rules a345–3593 a

Description 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_3000546nas 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/10.1145/2424321.242435201833nas 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_2001393nas 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)201201446nas 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/10.1145/1963405.196349601632nas 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 a

An 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.2011.01.00701673nas 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.pdf00780nas 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/2010/280401837nas a2200205 4500008004100000245007000041210006900111300001400180490000700194520119100201653002301392653003201415653000801447653002501455653001701480653003201497100001801529700002001547856006401567 2010 eng d00aConcept learning in description logics using refinement operators0 aConcept learning in description logics using refinement operator a203–2500 v783 a

With 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/c040n45u15qrnu44/01428nas a2200229 4500008004100000245006400041210006400105300001400169490000700183520074500190653002700935653002500962653003500987653002201022653001701044653002001061653001301081653001801094100002201112700002001134856004401154 2010 eng d00aGeneralized Distance Functions in the Theory of Computation0 aGeneralized Distance Functions in the Theory of Computation a443–4640 v533 a

We 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/bxm108