Efficient and self-tuning incremental query expansion for top-k query processing

  • Authors:

  • Martin Theobald;Ralf Schenkel;Gerhard Weikum

  • Affiliations:

  • Max Planck Institute for Informatics;Max Planck Institute for Informatics;Max Planck Institute for Informatics

  • Venue:
  • Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

We present a novel approach for efficient and self-tuning query expansion that is embedded into a top-k query processor with candidate pruning. Traditional query expansion methods select expansion terms whose thematic similarity to the original query terms is above some specified threshold, thus generating a disjunctive query with much higher dimensionality. This poses three major problems: 1) the need for hand-tuning the expansion threshold, 2) the potential topic dilution with overly aggressive expansion, and 3) the drastically increased execution cost of a high-dimensional query. The method developed in this paper addresses all three problems by dynamically and incrementally merging the inverted lists for the potential expansion terms with the lists for the original query terms. A priority queue is used for maintaining result candidates, the pruning of candidates is based on Fagin's family of top-k algorithms, and optionally probabilistic estimators of candidate scores can be used for additional pruning. Experiments on the TREC collections for the 2004 Robust and Terabyte tracks demonstrate the increased efficiency, effectiveness, and scalability of our approach.