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dc.contributor.authorWang, Zhe
dc.contributor.authorWang, Kewen
dc.contributor.authorZhuang, Zhiqiang
dc.contributor.authorQi, Guilin
dc.contributor.editorBlai Bonet, Sven Koenig
dc.date.accessioned2018-03-11T23:24:47Z
dc.date.available2018-03-11T23:24:47Z
dc.date.issued2015
dc.identifier.isbn9781577357001
dc.identifier.urihttp://hdl.handle.net/10072/125426
dc.description.abstractThe development and maintenance of large and complex ontologies are often time-consuming and error-prone. Thus, automated ontology learning and evolution have attracted intensive research interest. In data-centric applications where ontologies are designed from the data or automatically learnt from it, when new data instances are added that contradict the ontology, it is often desirable to incrementally revise the ontology according to the added data. In description logics, this problem can be intuitively formulated as the operation of TBox contraction, i.e., rational elimination of certain axioms from the logical consequences of a TBox, and it is w.r.t. an ABox. In this paper we introduce a model-theoretic approach to such a contraction problem by using an alternative semantic characterisation of DL-Lite TBoxes. We show that entailment checking (without necessarily first computing the contraction result) is in coNP, which does not shift the corresponding complexity in propositional logic, and the problem is tractable when the size of the new data is bounded.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAssociation for the Advancement of Artificial Intelligence
dc.publisher.placeUnited States
dc.publisher.urihttps://aaai.org/Conferences/conferences.php
dc.relation.ispartofconferencename29th Association-for-the-Advancement-of-Artificial-Intelligence (AAAI) Conference on Artificial Intelligence
dc.relation.ispartofconferencetitlePROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
dc.relation.ispartofdatefrom2015-01-25
dc.relation.ispartofdateto2015-01-30
dc.relation.ispartoflocationAustin, TX
dc.relation.ispartofpagefrom1656
dc.relation.ispartofpagefrom7 pages
dc.relation.ispartofpageto1662
dc.relation.ispartofpageto7 pages
dc.relation.ispartofvolume2
dc.subject.fieldofresearchArtificial intelligence not elsewhere classified
dc.subject.fieldofresearchcode460299
dc.titleInstance-driven Ontology Evolution in DL-Lite
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2015 AAAI Press. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
gro.hasfulltextFull Text
gro.griffith.authorWang, Kewen
gro.griffith.authorWang, Zhe


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