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
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info14050272

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.