01534nas 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-ontologies02265nas 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-water02311nas 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/S235286481730247X00883nas 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\_401448nas 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-far01783nas 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.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-2012-006601446nas 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 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.2011.01.00700780nas 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 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/c040n45u15qrnu44/01428nas 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/bxm10801540nas 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-2010-0010