01097nas 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.pdf01017nas 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 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_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 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.pdf01631nas 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/10.1145/2422085.242208701105nas 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_601166nas 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_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)201201837nas 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/