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

• Published in: Information (Basel) Vol. 14; no. 5; p. 272
• Main Authors: Chebil, Wiem, Wedyan, Mohammad, Alazab, Moutaz, Alturki, Ryan, Elshaweesh, Omar
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2023
• Subjects: Bayesian analysis; Bayesian networks; biomedical terminologies; Classifiers; Corpus linguistics; Deep learning; Documents; Information retrieval; Information storage and retrieval; Medical language; Medical research; Medicine; Methods; multinomial naive Bayesian classifier; Neural networks; Queries; Query expansion; Relevance feedback; Semantics; Terminology; Thesauri; Word sense disambiguation

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 Clini...