Effectively interpreting keyword queries on RDF databases with a rear view

  • Authors:

  • Haizhou Fu;Kemafor Anyanwu

  • Affiliations:

  • Semantic Computing Research Lab, Department of Computer Science, North Carolina State University, Raleigh NC;Semantic Computing Research Lab, Department of Computer Science, North Carolina State University, Raleigh NC

  • Venue:
  • ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
  • Year:
  • 2011

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Abstract

Effective techniques for keyword search over RDF databases incorporate an explicit interpretation phase that maps keywords in a keyword query to structured query constructs. Because of the ambiguity of keyword queries, it is often not possible to generate a unique interpretation for a keyword query. Consequently, heuristics geared toward generating the top-K likeliest user-intended interpretations have been proposed. However, heuristics currently proposed fail to capture any user-dependent characteristics, but rather depend on database-dependent properties such as occurrence frequency of subgraph pattern connecting keywords. This leads to the problem of generating top-K interpretations that are not aligned with user intentions. In this paper, we propose a context-aware approach for keyword query interpretation that personalizes the interpretation process based on a user's query context. Our approach addresses the novel problem of using a sequence of structured queries corresponding to interpretations of keyword queries in the query history as contextual information for biasing the interpretation of a new query. Experimental results presented over DBPedia dataset show that our approach outperforms the state-of-the-art technique on both efficiency and effectiveness, particularly for ambiguous queries.