{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T02:48:10Z","timestamp":1771728490169,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T00:00:00Z","timestamp":1683072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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 CT (Systematized Nomenclature of Medicine of Clinical Terms). Our approach, which is entitled improving semantic information retrieval (IMSIR), extracts and disambiguates concepts and retrieves documents. Relevant concepts of ambiguous terms were selected using probability measures and biomedical terminologies. Concepts are also extracted using an MNBC. The UMLS (Unified Medical Language System) thesaurus was then used to filter and rank concepts. Finally, we exploited a Bayesian network to match documents and queries using a conceptual representation. Our main contribution in this paper is to combine a supervised method (MNBC) and an unsupervised method (BN) to extract concepts from documents and queries. We also propose filtering the extracted concepts in order to keep relevant ones. Experiments of IMSIR using the two corpora, the OHSUMED corpus and the Clinical Trial (CT) corpus, were interesting because their results outperformed those of the baseline: the P@50 improvement rate was +36.5% over the baseline when the CT corpus was used.<\/jats:p>","DOI":"10.3390\/info14050272","type":"journal-article","created":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T01:33:36Z","timestamp":1683164016000},"page":"272","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Improving Semantic Information Retrieval Using Multinomial Naive Bayes Classifier and Bayesian Networks"],"prefix":"10.3390","volume":"14","author":[{"given":"Wiem","family":"Chebil","sequence":"first","affiliation":[{"name":"Department of Computer Science, Higher Institute of Computer Science of Mahdia, University of Monastir, Monastir 5000, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Wedyan","sequence":"additional","affiliation":[{"name":"Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2823-4776","authenticated-orcid":false,"given":"Moutaz","family":"Alazab","sequence":"additional","affiliation":[{"name":"Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0967-1885","authenticated-orcid":false,"given":"Ryan","family":"Alturki","sequence":"additional","affiliation":[{"name":"Department of Information Science, College of Computer and Information Systems, Umm Al-Qura University, P.O. Box 715, Makkah 21961, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omar","family":"Elshaweesh","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Information Technology College, Al-Hussein Bin Talal University, Ma\u2019an 71111, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1002\/asi.23435","article-title":"Indexing biomedical documents with a possibilistic network","volume":"67","author":"Chebil","year":"2016","journal-title":"J. Assoc. Inf. Sci. 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