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Combining Approximation and Relaxation in Semantic Web Path Queries

  • Alexandra Poulovassilis
  • Peter T. Wood
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6496)

Abstract

We develop query relaxation techniques for regular path queries and combine them with query approximation in order to support flexible querying of RDF data when the user lacks knowledge of its full structure or where the structure is irregular. In such circumstances, it is helpful if the querying system can perform both approximate matching and relaxation of the user’s query and can rank the answers according to how closely they match the original query. Our framework incorporates both standard notions of approximation based on edit distance and RDFS-based inference rules. The query language we adopt comprises conjunctions of regular path queries, thus including extensions proposed for SPARQL to allow for querying paths using regular expressions. We provide an incremental query evaluation algorithm which runs in polynomial time and returns answers to the user in ranked order.

Keywords

Regular Expression Edit Distance Conjunctive Query Path Query Triple Pattern 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alexandra Poulovassilis
    • 1
  • Peter T. Wood
    • 1
  1. 1.London Knowledge LabBirkbeck, University of LondonUK

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