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1.2 COMPUTER AND INFORMATION SCIENCE
This paper describes the benchmarking and analysis of five 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 five 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.
Kyle Goslin and Markus Hofmann. 2017. A Comparison of Automatic Search Query Enhancement Algorithms That Utilise Wikipedia as a Source of A Priori Knowledge. In FIRE’17: Forum for Information Retrieval Evaluation, December 8–10, 2017, Bangalore, India. ACM, New York, NY, USA, 8 pages. DOI: 10.1145/3158354.3158356