AAAI Publications, Twenty-Ninth AAAI Conference on Artificial Intelligence

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Approximating Model-Based ABox Revision in DL-Lite: Theory and Practice
Guilin Qi, Zhe Wang, Kewen Wang, Xuefeng Fu, Zhiqiang Zhuang

Last modified: 2015-02-09

Abstract


Model-based approaches provide a semantically well justified way to revise ontologies. However, in general, model-based revision operators are limited due to lack of efficient algorithms and inexpressibility of the revision results. In this paper, we make both theoretical and practical contribution to efficient computation of model-based revisions in DL-Lite. Specifically, we show that maximal approximations of two well-known model-based revisions for DL-Lite_R can be computed using a syntactic algorithm. However, such a coincidence of model-based and syntactic approaches does not hold when role functionality axioms are allowed. As a result, we identify conditions that guarantee such a coincidence for DL-Lite_FR. Our result shows that both model-based and syntactic revisions can co-exist seamlessly and the advantages of both approaches can be taken in one revision operator. Based on our theoretical results, we develop a graph-based algorithm for the revision operat

Keywords


Belief revision;Description logics;Ontology Evolution

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