Query Expansion based on Naive Bayes and Semantic Similarity

doi:10.23940/ijpe.18.07.p5.14211430

Abstract

Abstract:

A semantic query expansion method is put forward based on the comprehensive weighted algorithm of semantic similarity. We combine the ontology-based query expansion and corpus-based query expansion. If the query term matches the concept, we calculate the similarity between concepts, construct the connected graph of correlation among the ontology concepts, and expand the semantic query according to the threshold value. Otherwise, we adopt the Naive Bayes algorithm to calculate the co-occurrence probability between the word set and concepts as the relevancy of semantic query expansion. The experimental results show that this method can improve the retrieval performance effectively, with the Pr@30 index being improved by 41.97% compared to the traditional non-extensible query method.

Submitted on March 28, 2018; Revised on May 5, 2018; Accepted on June 21, 2018
References: 16

Zhiyun Zheng, Mengyao Yu, Ning Wang, Xingjin Zhang, Chunyang Ruan, and Dun Li. Query Expansion based on Naive Bayes and Semantic Similarity [J]. Int J Performability Eng, 2018, 14(7): 1421-1430.

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