Language models for keyword search over data graphs

  • Authors:

  • Yosi Mass;Yehoshua Sagiv

  • Affiliations:

  • IBM Haifa Research Lab, Haifa, & The Hebrew University, Jerusalem, Israel;The Hebrew University, Jerusalem, Israel

  • Venue:
  • Proceedings of the fifth ACM international conference on Web search and data mining
  • Year:
  • 2012

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Abstract

In keyword search over data graphs, an answer is a non-redundant subtree that includes the given keywords. This paper focuses on improving the effectiveness of that type of search. A novel approach that combines language models with structural relevance is described. The proposed approach consists of three steps. First, language models are used to assign dynamic, query-dependent weights to the graph. Those weights complement static weights that are pre-assigned to the graph. Second, an existing algorithm returns candidate answers based on their weights. Third, the candidate answers are re-ranked by creating a language model for each one. The effectiveness of the proposed approach is verified on a benchmark of three datasets: IMDB, Wikipedia and Mondial. The proposed approach outperforms all existing systems on the three datasets, which is a testament to its robustness. It is also shown that the effectiveness can be further improved by augmenting keyword queries with very basic knowledge about the structure.