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RDF Keyword Search Query Processing via Tensor Calculus

  • Roberto De Virgilio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7566)

Abstract

Keyword-based search over (semi)structured data is considered today an essential feature of modern information management systems and has become an hot area in database research and development. Answers to queries are generally sub-structures of a graph, containing one or more keywords. While finding the nodes matching keywords is relatively easy, determining the connections between such nodes is a complex problem requiring on-the-fly time consuming graph exploration. Current techniques suffer from poorly performing worst case scenario or from indexing schemes that provide little support to the discovery of connections between nodes. In this paper we propose an indexing scheme for RDF graphs based on the first principles of linear algebra, in particular on tensorial calculus. Leveraging our abstract algebraic framework, our technique allows to expedite the retrieval of the sub-structures representing the query results.

Keywords

Tensorial Representation Indexing Scheme Query Execution Hadamard Product Database Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Bondy, A., Murty, U.S.R.: Graph Theory. Springer (2010)Google Scholar
  2. 2.
    Davis, T.A.: Direct Methods for Sparse Linear Systems. SIAM (2006)Google Scholar
  3. 3.
    De Virgilio, R., Cappellari, P., Miscione, M.: Cluster-Based Exploration for Effective Keyword Search over Semantic Datasets. In: Laender, A.H.F., Castano, S., Dayal, U., Casati, F., de Oliveira, J.P.M. (eds.) ER 2009. LNCS, vol. 5829, pp. 205–218. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    He, H., Wang, H., Yang, J., Yu, P.S.: Blinks: ranked keyword searches on graphs. In: SIGMOD, pp. 305–316 (2007)Google Scholar
  5. 5.
    Tian, Y., Patel, J.M.: Tale: A tool for approximate large graph matching. In: ICDE (2008)Google Scholar
  6. 6.
    Tran, T., Wang, H., Rudolph, S., Cimiano, P.: Top-k exploration of query candidates for efficient keyword search on graph-shaped (rdf) data. In: ICDE, pp. 405–416 (2009)Google Scholar
  7. 7.
    Zhang, S., Hu, M., Yang, J.: Treepi: A novel graph indexing method. In: ICDE (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Roberto De Virgilio
    • 1
  1. 1.Dipartimento di Informatica e AutomazioneUniversitá Roma TreRomeItaly

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