Can phrase indexing help to process non-phrase queries?

  • Authors:

  • Mingjie Zhu;Shuming Shi;Nenghai Yu;Ji-Rong Wen

  • Affiliations:

  • University of Science and Technology of China, Hefei, China;Microsoft Research Asia, Beijing, China;University of Science and Technology of China, Hefei, China;Microsoft Research Asia, Beijing, China

  • Venue:
  • Proceedings of the 17th ACM conference on Information and knowledge management
  • Year:
  • 2008

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Abstract

Modern web search engines, while indexing billions of web pages, are expected to process queries and return results in a very short time. Many approaches have been proposed for efficiently computing top-k query results, but most of them ignore one key factor in the ranking functions of commercial search engines - term-proximity, which is the metric of the distance between query terms in a document. When term-proximity is included in ranking functions, most of the existing top-k algorithms will become inefficient. To address this problem, in this paper we propose to build a compact phrase index to speed up the search process when incorporating the term-proximity factor. The compact phrase index can help more accurately estimate the score upper bounds of unknown documents. The size of the phrase index is controlled by including a small portion of phrases which are possibly helpful for improving search performance. Phrase index has been used to process phrase queries in existing work. It is, however, to the best of our knowledge, the first time that phrase index is used to improve the performance of generic queries. Experimental results show that, compared with the state-of-the-art top-k computation approaches, our approach can reduce average query processing time to 1/5 for typical setttings.