Effective and efficient keyword query interpretation using a hybrid graph

  • Authors:

  • Junquan Chen;Kaifeng Xu;Haofen Wang;Wei Jin;Yong Yu

  • Affiliations:

  • Apex Data and Knowledge Management Lab, Shanghai Jiao Tong University, Shanghai, China;Apex Data and Knowledge Management Lab, Shanghai Jiao Tong University, Shanghai, China;Apex Data and Knowledge Management Lab, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science, North Dakota State University, Fargo, ND;Apex Data and Knowledge Management Lab, Shanghai Jiao Tong University, Shanghai, China

  • Venue:
  • WISE'10 Proceedings of the 11th international conference on Web information systems engineering
  • Year:
  • 2010

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Abstract

Empowering users to access RDF data using keywords can relieve them from the steep learning curve of mastering a structured query language and understanding complex and possibly fast evolving data schemas. In recent years, translating keywords into SPARQL queries has been widely studied. Approaches relying on the original RDF graph (instance-based approaches) usually generate precise query interpretations at the cost of a long processing time while those relying on the summary graph extracted from RDF data (schema-based approaches) significantly speed up query interpretation disregarding the loss of accuracy. In this paper, we propose a novel approach based on a hybrid graph, for the trade-off between interpretation accuracy and efficiency. The hybrid graph can preserve most of the connectivity information of the corresponding instance graph in a small size. We conduct experiments on three widely-used data sets of different sizes. The results show that our approach can achieve significant efficiency improvement with a limited accuracy drop compared with instance-based approaches, and meanwhile, can achieve promising accuracy gain at an affordable time cost compared with schema-based approaches.