SPARK: adapting keyword query to semantic search

  • Authors:

  • Qi Zhou;Chong Wang;Miao Xiong;Haofen Wang;Yong Yu

  • Affiliations:

  • Apex Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China;Apex Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China;Apex Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China;Apex Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China;Apex Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, P.R. China

  • Venue:
  • ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
  • Year:
  • 2007

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Abstract

Semantic search promises to provide more accurate result than present-day keyword search. However, progress with semantic search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform semantic search. A prototype system named 'SPARK' has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result.