dc.contributor.author | Wang, Zhe | |
dc.contributor.author | Wang, Kewen | |
dc.contributor.author | Zhuang, Zhiqiang | |
dc.contributor.author | Qi, Guilin | |
dc.contributor.editor | Blai Bonet, Sven Koenig | |
dc.date.accessioned | 2018-03-11T23:24:47Z | |
dc.date.available | 2018-03-11T23:24:47Z | |
dc.date.issued | 2015 | |
dc.identifier.isbn | 9781577357001 | |
dc.identifier.uri | http://hdl.handle.net/10072/125426 | |
dc.description.abstract | The 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.peerreviewed | Yes | |
dc.language | English | |
dc.publisher | Association for the Advancement of Artificial Intelligence | |
dc.publisher.place | United States | |
dc.publisher.uri | https://aaai.org/Conferences/conferences.php | |
dc.relation.ispartofconferencename | 29th Association-for-the-Advancement-of-Artificial-Intelligence (AAAI) Conference on Artificial Intelligence | |
dc.relation.ispartofconferencetitle | PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | |
dc.relation.ispartofdatefrom | 2015-01-25 | |
dc.relation.ispartofdateto | 2015-01-30 | |
dc.relation.ispartoflocation | Austin, TX | |
dc.relation.ispartofpagefrom | 1656 | |
dc.relation.ispartofpagefrom | 7 pages | |
dc.relation.ispartofpageto | 1662 | |
dc.relation.ispartofpageto | 7 pages | |
dc.relation.ispartofvolume | 2 | |
dc.subject.fieldofresearch | Artificial intelligence not elsewhere classified | |
dc.subject.fieldofresearchcode | 460299 | |
dc.title | Instance-driven Ontology Evolution in DL-Lite | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dc.type.code | E - Conference Publications | |
dc.description.version | Accepted Manuscript (AM) | |
gro.faculty | Griffith 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.hasfulltext | Full Text | |
gro.griffith.author | Wang, Kewen | |
gro.griffith.author | Wang, Zhe | |