Geographical Queries Reformulation using Parallel FP-Growth for Spatial Taxonomies Building

摘要

Due to its specificities and hierarchical structure, a geographical query needs a special process of reformulation by Information Retrieval Systems (IRS). This fact is ignored by most of web search engines. In this paper, we propose an automatic approach for building a spatial taxonomy that models' the notion of adjacency that can be uses in the reformulation of the spatial part of a geographical query. This approach exploits the documents that are in the top of the list of retrieved results when submitting a spatial entity, which is composed of a spatial relation and a noun of a city. Then, a transactional database is constructed, considering each document extracted as a transaction that contains the nouns of the cities sharing the country of the submitted query's city. The algorithm FP-Growth is applied to this database in his parallel version (PFP) in order to generate association rules, that will form the country's taxonomy in a Big Data context. Experiments has been conducted on Spark and their results show that query reformulation based on the taxonomy constructed using our proposed approach improves the precision and the effectiveness of the IRS.