Biomedical Concepts Extraction Based on Possibilistic Network and Vector Space Model

Wiem Chebil; Lina Fatima Soualmia; Mohamed Nazih Omri; Stéfan Jacques Darmoni

doi:10.1007/978-3-319-19551-3_29

Conference papers

Biomedical Concepts Extraction Based on Possibilistic Network and Vector Space Model

Wiem Chebil ^{1, 2} Lina Fatima Soualmia ^{1, 3, 4} Mohamed Nazih Omri ² Stéfan Jacques Darmoni ^{1, 4}

1 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes

2 UM - Université de Monastir - University of Monastir

3 TIBS - LITIS - Equipe Traitement de l'information en Biologie Santé

LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes

4 LIMICS - Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé

Abstract : This paper proposes a new approach for indexing biomedical documents based on the combination of a Possibilistic Network and a Vector Space Model. This later carries out partial matching between documents and biomedical vocabularies. The main contribution of the proposed approach is to combine the cosine similarity and the two measures of possibility and necessity to enhance the estimation of the similarity between a document and a given concept. The possibility estimates the extent to which a document is not similar to the concept. The necessity allows the confirmation that the document is similar to the concept. Experiments were carried out on the OSHUMED corpora and showed encouraging results.

Keywords : Indexing Biomedical documents Possibilistic network Vector space model Partial matching

Document type :

Conference papers

Domain :

Computer Science [cs]

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https://hal-normandie-univ.archives-ouvertes.fr/hal-02100421
Contributor : Lina F Soualmia Connect in order to contact the contributor
Submitted on : Monday, April 15, 2019 - 7:32:01 PM
Last modification on : Friday, December 3, 2021 - 11:43:06 AM

Biomedical Concepts Extraction Based on Possibilistic Network and Vector Space Model

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Biomedical Concepts Extraction Based on Possibilistic Network and Vector Space Model

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