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International Conference on Database Systems for Advanced Applications

DASFAA 2012: Database Systems for Advanced Applications pp 141–155Cite as

Partitioned Indexes for Entity Search over RDF Knowledge Bases

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7238))

Abstract

The rapid growth of RDF data in RDF knowledge bases calls for efficient query processing techniques. This paper focuses on the star-style SPARQL join queries, which is very common when users want to search information of entities from RDF knowledge bases. We observe that the computational cost of such queries mainly comes from loading a large portion of predicate-ahead indexes. We therefore propose to partition the whole RDF knowledge bases based on the schema of individual entities, so that only entities of similar schemas are allocated into the same cluster. Such a partitioning strategy generates a pruning mechanism that effectively isolate the correlations of partitions and the queries. Consequently, queries are only conducted over a small number of partitions with small predicate-ahead indexes. Experiments over a large real-life RDF data set show the significant performance improvements achieved by our partitioned indexing techniques.

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Author information

Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, China

    Fang Du & Xiaoyong Du

  2. Key Laboratory of Data Engineering and Knowledge Engineering, (Renmin University of China), MOE, China

    Yueguo Chen & Xiaoyong Du

Authors
  1. Fang Du
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  2. Yueguo Chen
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  3. Xiaoyong Du
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Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, Seoul National University, Gwanak-ro, Gwanak-gu, 151747, Seoul, South Korea

    Sang-goo Lee

  2. Computer School, Wuhan University, Luo-jia-shan, Wuchang, 430081, Wuhan, Hubei Province, China

    Zhiyong Peng

  3. School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Brisbane, Australia

    Xiaofang Zhou

  4. Department of Computer Science, Kangwon National University, 192-1, Hyoja2-Dong, Chuncheon, 200701, Kangwon, South Korea

    Yang-Sae Moon

  5. Institute for Computer Science and Business Information, University of Duisburg-Essen, Schützenbahn 70, 45117, Essen, Germany

    Rainer Unland

  6. School of Information and Communication Engineering, Chungbuk National University, 52 Naesudong-ro, Heungdeok-gu, Cheongju, 4072, Chungbuk, South Korea

    Jaesoo Yoo

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© 2012 Springer-Verlag Berlin Heidelberg

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Du, F., Chen, Y., Du, X. (2012). Partitioned Indexes for Entity Search over RDF Knowledge Bases. In: Lee, Sg., Peng, Z., Zhou, X., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29038-1_12

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  • DOI: https://doi.org/10.1007/978-3-642-29038-1_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29037-4

  • Online ISBN: 978-3-642-29038-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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