Graph-based approaches to debugging and revision of terminologies in DL-Lite

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• 作者：Xuefeng Fu ^{fxf@seu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Guilin Qi ; ^{gqi@seu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; ^{fxfcn@126.com" class="auth_mail" title="E-mail the corresponding author} ; Yong Zhang ^{zhangyong@seu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Zhangquan Zhou ^{quanzz@seu.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：DL-Lite ; Ontology debugging ; Ontology revision ; Scoring function ; Hitting set tree
• 刊名：Knowledge-Based Systems
• 出版年：2016
• 出版时间：15 May 2016
• 年：2016
• 卷：100
• 期：Complete
• 页码：1-12
• 全文大小：1005 K

文摘

In this paper, we deal with the problem of debugging and revision of incoherent terminologies. Ontology debugging aims to provide the explanation of the causes of incoherence and ontology revision aims to eliminate the incoherence. For this purpose, we propose the graph-based approaches to deal with the debugging and revision of terminologies for a family of lightweight ontology languages, DL-Lite. First of all, we transform DL-Lite ontologies to graphs. To deal with the problem of ontology debugging, we calculate the minimal incoherence-preserving subsets (MIPS) of an ontology by computing the minimal incoherence-preserving path-pairs (MIPP) based on the transformed graph. To deal with the problem of ontology revision, we propose the notion of revision state which separates the terminology of an ontology into two disjoint sets: the set of wanted axioms and the set of unwanted axioms. We further define a revision operator based on the revision state. Afterward, two revision algorithms are proposed to instantiate the revision operator: one is based on a scoring function, and the other one is based on a hitting set tree. We implement these algorithms and conduct experiments of ontology debugging and ontology revision on several adapted real ontologies. The experimental results of ontology debugging show that our approach of calculating MIPS based on graph is efficient and outperforms the state of the art. The experimental results of ontology revision show that the algorithm based on a scoring function is more efficient than the algorithm based on a hitting set tree.