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An Optimization Technique for RDFS Inference the Applied Order of RDF Schema Entailment Rules  

Kim, Ki-Sung (서울대학교 컴퓨터공학부)
Yoo, Sang-Won (서울대학교 컴퓨터공학부)
Lee, Tae-Whi (서울대학교 컴퓨터공학부)
Kim, Hyung-Joo (서울대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Databases / v.33, no.2, 2006 , pp. 151-162 More about this Journal
Abstract
RDF Semantics, one of W3C Recommendations, provides the RDFS entailment rules, which are used for the RDFS inference. Sesame, which is well known RDF repository, supports the RDBMS-based RDFS inference using the forward-chaining strategy. Since inferencing in the forward-chaining strategy is performed in the data loading time, the data loading time in Sesame is slow down be inferencing. In this paper, we propose the order scheme for applying the RDFS entailment rules to improve inference performance. The proposed application order makes the inference process terminate without repetition of the process for most cases and guarantees the completeness of inference result. Also the application order helps to reduce redundant results during the inference by predicting the results which were made already by previously applied rules. In this paper, we show that our approaches can improve the inference performance with comparisons to the original Sesame using several real-life RDF datasets.
Keywords
Semantic Web; RDF; Rule-based Inference; Forward-chaining;
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