Geographical Query reformulation using a Geographical Taxonomy and WordNet

Midaoui, Omar El; Ghali, Btihal El; Qadi, Abderrahim El; Rahmani, Moulay Driss

doi:10.1016/j.procs.2018.01.147

Cited by 7 publications

(7 citation statements)

References 6 publications

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“…As a pre-treatment step, we deleted accents from the extracted documents to minimize the matching gab between ASEs, due to different manners of writing cities' names by the persons who wrote the documents contents. Because, we have noticed before that the miss-matching problem arise particularly in the case of nouns that contain accents [25]. Then, we varied the SR of the spatial entities submitted to verify if the variation of the SR influences the performance of the proposed approach.…”

Section: Experimentation Resultsmentioning

confidence: 99%

Geographical queries reformulation using a parallel association rules generator to build spatial taxonomies

Midaoui

Ghali

Qadi

et al. 2021

IJECE

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Geographical queries need a special process of reformulation by information retrieval systems (IRS) due to their specificities and hierarchical structure. 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 will be used in the reformulation of the spatial part of a geographical query. This approach exploits the documents that are in top of the retrieved list 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 frequent pattern growth (FP-growth) is applied to this database in his parallel version (parallel FP-growth: 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 using the taxonomy constructed based on our proposed approach improves the precision and the effectiveness of the IRS.

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Section: Experimentation Resultsmentioning

confidence: 99%

Geographical queries reformulation using a parallel association rules generator to build spatial taxonomies

Midaoui

Ghali

Qadi

et al. 2021

IJECE

Self Cite

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“…However, the shortcomings include ambiguous terms and a lack of unique ontological properties causes more complexities. Zingla et al and Omar et al proposed hybrid models for extracting expansion concepts from DBpedia and Wikipedia [ 8 , 9 ]. They employed Microblog and TREC 2011 datasets for evaluating their ontological performance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For instance, a search engine can fetch approximately one million webpages for a user query. Organizations apply business intelligence (BI) tools to process a large amount of data and retrieve valuable information [6][7][8][9][10][11]. To compete e ectively, organizations should analyze and leverage a wide range of data, information, and expertise in order to make e ective decisions.…”

Section: Introductionmentioning

confidence: 99%

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Alothman

Sait

2022

Computational Intelligence and Neuroscience

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In recent times, artificial intelligence (AI) methods have been applied in document and content management to make decisions and improve the organization’s functionalities. However, the lack of semantics and restricted metadata hinders the current document management technique from achieving a better outcome. E-Government activities demand a sophisticated approach to handle a large corpus of data and produce valuable insights. There is a lack of methods to manage and retrieve bilingual (Arabic and English) documents. Therefore, the study aims to develop an ontology-based AI framework for managing documents. A testbed is employed to simulate the existing and proposed framework for the performance evaluation. Initially, a data extraction methodology is utilized to extract Arabic and English content from 77 documents. Researchers developed a bilingual dictionary to teach the proposed information retrieval technique. A classifier based on the Naïve Bayes approach is designed to identify the documents’ relations. Finally, a ranking approach based on link analysis is used for ranking the documents according to the users’ queries. The benchmark evaluation metrics are applied to measure the performance of the proposed ontological framework. The findings suggest that the proposed framework offers supreme results and outperforms the existing framework.

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“…The expansion method used for improving the thematic part of our queries is proposed based on the works of Nakade et al (2018) and Midaoui et al (2018), which proposes a semantic query expansion approach that retrieves relevant tweets. This approach uses the thesaurus of thesaurus.com to search for synonyms for original search topics and reformulate a query by adding synonyms and search topics using parenthesis and OR operators.…”

Section: Introductionmentioning

confidence: 99%

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

Midaoui

Ghali

Qadi

et al. 2021

JSIT

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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.

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Geographical Query reformulation using a Geographical Taxonomy and WordNet

Cited by 7 publications

References 6 publications

Geographical queries reformulation using a parallel association rules generator to build spatial taxonomies

Geographical queries reformulation using a parallel association rules generator to build spatial taxonomies

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

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

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