A comparison of automatic search qery enhancement algorithms that utilise Wikipedia as a source of a priori knowledge

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Abstract

This paper describes the benchmarking and analysis of fve Automatic Search Query Enhancement (ASQE) algorithms that utilise Wikipedia as the sole source for a priori knowledge. The contributions of this paper include: 1) A comprehensive review into current ASQE algorithms that utilise Wikipedia as the sole source for a priori knowledge; 2) benchmarking of fve existing ASQE algorithms using the TREC-9 Web Topics on the ClueWeb12 data set and 3) analysis of the results from the benchmarking process to identify the strengths and weaknesses each algorithm. During the benchmarking process, 2,500 relevance assessments were performed. Results of these tests are analysed using the Average Precision @10 per query and Mean Average Precision @10 per algorithm. From this analysis we show that the scope of a priori knowledge utilised during enhancement and the available term weighting methods available from Wikipedia can further aid the ASQE process. Although approaches taken by the algorithms are still relevant, an over dependence on weighting schemes and data sources used can easily impact results of an ASQE algorithm.

Original languageEnglish
Title of host publicationFIRE 2017 - Proceedings of the 9th Annual Meeting of the Forum for Information Retrieval Evaluation
EditorsMandar Mitra, Jainisha Sankhavara, Prasenjit Majumder, Parth Mehta
PublisherAssociation for Computing Machinery
Pages6-13
Number of pages8
ISBN (Electronic)9781450363822
DOIs
Publication statusPublished - 8 Dec 2017
Event9th Annual Meeting of the Forum for Information Retrieval Evaluation, FIRE 2017 - Bangalore, India
Duration: 8 Dec 201710 Dec 2017

Publication series

NameACM International Conference Proceeding Series

Conference

Conference9th Annual Meeting of the Forum for Information Retrieval Evaluation, FIRE 2017
Country/TerritoryIndia
CityBangalore
Period8/12/1710/12/17

Keywords

  • Information retrieval
  • Search Query Enhancement
  • Wikipedia

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