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Mapping Keywords to Linked Data Resources for Automatic Query Expansion

  • Isabelle Augenstein
  • Anna Lisa Gentile
  • Barry Norton
  • Ziqi Zhang
  • Fabio Ciravegna
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7955)

Abstract

Linked Data is a gigantic, constantly growing and extremely valuable resource, but its usage is still heavily dependent on (i) the familiarity of end users with RDF’s graph data model and its query language, SPARQL, and (ii) knowledge about available datasets and their contents. Intelligent keyword search over Linked Data is currently being investigated as a means to overcome these barriers to entry in a number of different approaches, including semantic search engines and the automatic conversion of natural language questions into structured queries. Our work addresses the specific challenge of mapping keywords to Linked Data resources, and proposes a novel method for this task. By exploiting the graph structure within Linked Data we determine which properties between resources are useful to discover, or directly express, semantic similarity. We also propose a novel scoring function to rank results. Experiments on a publicly available dataset show a 17% improvement in Mean Reciprocal Rank over the state of the art.

Keywords

Semantic Similarity Query Expansion Mean Reciprocal Rank Labelling Property Natural Language Interface 
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 2013

Authors and Affiliations

  • Isabelle Augenstein
    • 1
  • Anna Lisa Gentile
    • 1
  • Barry Norton
    • 2
  • Ziqi Zhang
    • 1
  • Fabio Ciravegna
    • 1
  1. 1.Department of Computer ScienceUniversity of SheffieldUK
  2. 2.OntotextUK

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