Query expansion using topic and location

Shu Huang, Qiankun Zhao, Prasenjit Mitra, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Users use a few keywords to post queries to search engines. Search engines, often, fail to return answers that their users seek because the keyword queries incompletely specify the information being sought and because of the ambiguity of natural language terms. Query expansion, where additional keywords are added automatically or semi-automatically to the user's query before it is run, has been used to improve the accuracy of search engines. We propose a framework where first, we identify whether a query should be expanded based on its features. We focus on identifying queries whose results are location-sensitive and expand them using keywords from similar queries from similar locations. Similarity between queries is derived using a novel LDA-based topic-level query similarity measure. We conducted experiments with query log data from the CiteSeer digital library and see a small improvement of results due to our query expansion.

Original languageEnglish
Title of host publicationICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops
Pages619-624
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2007
Event17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 - Omaha, NE, United States
Duration: 28 Oct 200731 Oct 2007

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007
CountryUnited States
CityOmaha, NE
Period28/10/0731/10/07

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Huang, S., Zhao, Q., Mitra, P., & Giles, C. L. (2007). Query expansion using topic and location. In ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops (pp. 619-624). [4476732] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDMW.2007.116