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A Scalable Change Detection Technique for RDF Data using a Backward-chaining Inference based on Relational Databases  

Im, Dong-Hyuk (서울대학교 컴퓨터공학부)
Lee, Sang-Won (성균관대학교 정보통신공학부)
Kim, Hyoung-Joo (서울대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Databases / v.37, no.4, 2010 , pp. 197-202 More about this Journal
Abstract
Recent studies on change detection for RDF data are focused on not only the structural difference but also the semantic-aware difference by computing the closure of RDF models. However, since these techniques which take into account the semantics of RDF model require both RDF models to be memory resident, or they use a forward-chaining strategy which computes the entire closure in advance, it is not efficient to apply them directly to detect changes in large RDF data. In this paper, we propose a scalable change detection technique for RDF data, which uses a backward-chaining inference based on relational database. Proposed method uses a new approach for RDF reasoning that computes only the relevant part of the closure for change detection in a relational database. We show that our method clearly outperforms the previous works through experiment using the real RDF from the bioinformatics domain.
Keywords
RDF; change detection; inference; relational database;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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