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DC Proposal: Automatically Transforming Keyword Queries to SPARQL on Large-Scale Knowledge Bases

  • Saeedeh Shekarpour
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7032)

Abstract

Most Web of Data applications focus mainly on using SPARQL for issuing queries. This leads to the Web of Data being difficult to access for non-experts. Another problem that will intensify this challenge is when applying the algorithms on large-scale and decentralized knowledge bases. In the current thesis, firstly we focus on the methods for transforming keyword-based queries into SPARQL automatically. Secondly, we will work on improving those methods in order to apply them on (a large subset of) the Linked Data Web. In an early phase, a heuristic method was proposed for generating SPARQL queries out of arbitrary number of keywords. Its preliminary evaluation showed promising results. So, we are working on the possible improvements for applying that on the large-scale knowledge bases.

Keywords

Keyword Search User Query Conjunctive Query SPARQL 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 2011

Authors and Affiliations

  • Saeedeh Shekarpour
    • 1
  1. 1.Institut für Informatik, AKSWUniversität LeipzigLeipzigGermany

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