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Authors: Omar El Midaoui 1 ; Btihal El Ghali 2 and Abderrahim El Qadi 3

Affiliations: 1 LRIT Associated Unit to the CNRST - URAC n°29, Faculty of Sciences, Mohammed V University in Rabat, Morocco, SmartiLab, Ecole Marocaine des Sciences de l'Ingénieur (EMSI), Rabat, Morocco ; 2 SmartiLab, Ecole Marocaine des Sciences de l'Ingénieur (EMSI), Rabat, Morocco ; 3 TIM, High School of Technology, Mohammed V University in Rabat, Morocco

Keyword(s): Information Retrieval, Parallel FP-Growth Algorithm, Machine Learning, Geographical Query Reformulation, Spatial Entity, Spark, Big Data.

Abstract: 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. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
El Midaoui, O.; El Ghali, B. and El Qadi, A. (2020). Geographical Queries Reformulation using Parallel FP-Growth for Spatial Taxonomies Building. In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - IoTBDS; ISBN 978-989-758-426-8; ISSN 2184-4976, SciTePress, pages 375-381. DOI: 10.5220/0009446603750381

@conference{iotbds20,
author={Omar {El Midaoui}. and Btihal {El Ghali}. and Abderrahim {El Qadi}.},
title={Geographical Queries Reformulation using Parallel FP-Growth for Spatial Taxonomies Building},
booktitle={Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - IoTBDS},
year={2020},
pages={375-381},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009446603750381},
isbn={978-989-758-426-8},
issn={2184-4976},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - IoTBDS
TI - Geographical Queries Reformulation using Parallel FP-Growth for Spatial Taxonomies Building
SN - 978-989-758-426-8
IS - 2184-4976
AU - El Midaoui, O.
AU - El Ghali, B.
AU - El Qadi, A.
PY - 2020
SP - 375
EP - 381
DO - 10.5220/0009446603750381
PB - SciTePress