XML-based RDF data management for efficient query processing

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Abstract

The Semantic Web, which represents a web of knowledge, offers new opportunities to search for knowledge and information. To harvest such search power requires robust and scalable data repositories that can store RDF data and support efficient evaluation of SPARQL queries. Most of the existing RDF storage techniques rely on relation model and relational database technologies for these tasks. They either keep the RDF data as triples, or decompose it into multiple relations. The mis-match between the graph model of the RDF data and the rigid 2D tables of relational model jeopardizes the scalability of such repositories and frequently renders a repository inefficient for some types of data and queries. We propose to decompose RDF graph into a forest of semantically correlated XML trees, store them in an XML repository and rewrite SPARQL queries into XPath/XQuery queries to be evaluated in the XML repository. In this paper, we discuss the basic idea of RDF-to-XML decomposition and the criteria of such decomposition in term of correctness, redundancy and query efficiency, then propose two RDF-to-XML decomposition algorithms based on these criteria. Our experimental evaluation results illustrate that our approach is capable of improving both the storage efficiency and query processing efficiency compared to the existing RDF techniques.

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Zhou, M., & Wu, Y. (2010). XML-based RDF data management for efficient query processing. In Proceedings of the ACM SIGMOD International Conference on Management of Data. Association for Computing Machinery. https://doi.org/10.1145/1859127.1859132

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