DivQ: diversification for keyword search over structured databases

  • Authors:

  • Elena Demidova;Peter Fankhauser;Xuan Zhou;Wolfgang Nejdl

  • Affiliations:

  • L3S Research Center, Hannover, Germany;L3S Research Center, Hannover, Germany;CSIRO ICT Centre, Canberra, Australia;L3S Research Center, Hannover, Germany

  • Venue:
  • Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2010

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Abstract

Keyword queries over structured databases are notoriously ambiguous. No single interpretation of a keyword query can satisfy all users, and multiple interpretations may yield overlapping results. This paper proposes a scheme to balance the relevance and novelty of keyword search results over structured databases. Firstly, we present a probabilistic model which effectively ranks the possible interpretations of a keyword query over structured data. Then, we introduce a scheme to diversify the search results by re-ranking query interpretations, taking into account redundancy of query results. Finally, we propose α-nDCG-W and WS-recall, an adaptation of α-nDCG and S-recall metrics, taking into account graded relevance of subtopics. Our evaluation on two real-world datasets demonstrates that search results obtained using the proposed diversification algorithms better characterize possible answers available in the database than the results of the initial relevance ranking.