Jia Xu, Patrick Shironoshita, Ubbo Visser, Nigel John, Mansur Kabuka
{"title":"Optimizing the Most Specific Concept Method for Efficient Instance Checking.","authors":"Jia Xu, Patrick Shironoshita, Ubbo Visser, Nigel John, Mansur Kabuka","doi":"10.1145/2567948.2577294","DOIUrl":null,"url":null,"abstract":"<p><p>Instance checking is considered a central tool for data retrieval from description logic (DL) ontologies. In this paper, we propose a revised most specific concept (MSC) method for DL <i>SHI</i>, which converts instance checking into subsumption problems. This revised method can generate small concepts that are specific-enough to answer a given query, and allow reasoning to explore only a subset of the ABox data to achieve efficiency. Experiments show effectiveness of our proposed method in terms of concept size reduction and the improvement in reasoning efficiency.</p>","PeriodicalId":74532,"journal":{"name":"Proceedings of the ... International World-Wide Web Conference. International WWW Conference","volume":"2014 ","pages":"405-406"},"PeriodicalIF":0.0000,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2567948.2577294","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... International World-Wide Web Conference. International WWW Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2567948.2577294","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Instance checking is considered a central tool for data retrieval from description logic (DL) ontologies. In this paper, we propose a revised most specific concept (MSC) method for DL SHI, which converts instance checking into subsumption problems. This revised method can generate small concepts that are specific-enough to answer a given query, and allow reasoning to explore only a subset of the ABox data to achieve efficiency. Experiments show effectiveness of our proposed method in terms of concept size reduction and the improvement in reasoning efficiency.