Improving Semantic Information Retrieval Using Multinomial Naive Bayes Classifier and Bayesian Networks

Autor: Wiem Chebil, Mohammad Wedyan, Moutaz Alazab, Ryan Alturki, Omar Elshaweesh

Publikováno v: Information, Vol 14, Iss 5, p 272 (2023)

This research proposes a new approach to improve information retrieval systems based on a multinomial naive Bayes classifier (MNBC), Bayesian networks (BNs), and a multi-terminology which includes MeSH thesaurus (Medical Subject Headings) and SNOMED

Externí odkaz: https://doaj.org/article/038ecd3af2364d6993fb6518be5db640

Context-Aware Personalized Web Search Using Navigation History

Autor: Mohammad Wedyan, Haiyan Lu, Omar Ghaleb Elshaweesh, Wiem Chebil

It is highly desirable that web search engines know users well and provide just what the user needs. Although great effort has been devoted to achieve this dream, the commonly used web search engines still provide a “one-fit-all” results. One of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d473e4931a75a56e66d1955a23742780
https://hdl.handle.net/10453/144002

Indexing biomedical documents with a possibilistic network

Autor: Wiem Chebil, Mohamed Nazih Omri, Lina Fatima Soualmia, Stéfan Jacques Darmoni

Publikováno v: Journal of the Association for Information Science and Technology
Journal of the Association for Information Science and Technology, ASIS&T/Wiley, 2016, 67 (4), pp.928-941. ⟨10.1002/asi.23435⟩

International audience; In this article, we propose a new approach for indexing biomedical documents based on a possibilistic network that carries out partial matching between documents and biomedical vocabulary. The main contribution of our approach

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94de64058fd46ad87731642d60e07c3e
https://doi.org/10.1002/asi.23435

Indexing biomedical documents with Bayesian networks and terminologies

Autor: Mohamed Nazih Omri, Lina Fatima Soualmia, Stéfan Jacques Darmoni, Wiem Chebil

Publikováno v: 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)
2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nov 2017, Nanjing, China. ⟨10.1109/ISKE.2017.8258745⟩
ISKE

We proposed a new approach denoted SDIBN (Semantic Documents Indexing using Bayesian Networks) for indexing biomedical documents with terminologies. The main contribution of SDIBN is to use Bayesian Networks (BN) and the probability inference to perf

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e2e004c816d5e9a29bd68de13d418a69
https://inria.hal.science/hal-01848617

Biomedical Concepts Extraction Based on Possibilistic Network and Vector Space Model

Autor: Mohamed Nazih Omri, Lina Fatima Soualmia, Stéfan Jacques Darmoni, Wiem Chebil

Publikováno v: Artificial Intelligence in Medicine-15th Conference on Artificial Intelligence in Medicine
Artificial Intelligence in Medicine-15th Conference on Artificial Intelligence in Medicine, Jun 2015, Pavia, Italy. pp.227-231, ⟨10.1007/978-3-319-19551-3_29⟩
Artificial Intelligence in Medicine ISBN: 9783319195506
AIME

International audience; 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 vocabu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7bb3caf1bf187b8d9cf5551a4cac3d4
https://hal-normandie-univ.archives-ouvertes.fr/hal-02100421