Geographical query reformulation using a geographical adjacency taxonomy builder and word senses

Abstract

Purpose

Geographical query formulation is one of the key difficulties for users in search engines. The purpose of this study is to improve geographical search by proposing a novel geographical query reformulation (GQR) technique using a geographical taxonomy and word senses.

Design/methodology/approach

This work introduces an approach for GQR, which combines a method of query components separation that uses GeoNames, a technique for reformulating these components using WordNet and a geographic taxonomy constructed using the latent semantic analysis method.

Findings

The proposed approach was compared to two methods from the literature, using the mean average precision (MAP) and the precision at 20 documents (P@20). The experimental results show that it outperforms the other techniques by 15.73% to 31.21% in terms of P@20 and by 17.81% to 35.52% in terms of MAP.

Research limitations/implications

According to the experimental results, the best created taxonomy using the geographical adjacency taxonomy builder contains 7.67% of incorrect links. This paper believes that using a very big amount of data for taxonomy building can give better results. Thus, in future work, this paper intends to apply the approach in a big data context.

Originality/value

Despite this, the reformulation of geographical queries using the new proposed approach considerably improves the precision of queries and retrieves relevant documents that were not retrieved using the original queries. The strengths of the technique lie in the facts of reformulating both thematic and spatial entities and replacing the spatial entity of the query with terms that explain the intent of the query more precisely using a geographical taxonomy.

Citation

El Midaoui, O., El Ghali, B., El Qadi, A. and Rahmani, M.D. (2021), "Geographical query reformulation using a geographical adjacency taxonomy builder and word senses", Journal of Systems and Information Technology, Vol. 23 No. 1, pp. 1-19. https://doi.org/10.1108/JSIT-02-2018-0022