Ontology-based Keyword Enriched Topic Modelling for Biomedical Research Trends: A Business Intelligence Perspective

Abstract

Over the past ten years, the biomedical literature has increased exponentially, and it is becoming increasingly cumbersome for the researchers and policymakers to identify emerging directions in research and allocate resources appropriately. Bibliometric indicators that are traditional and topic models based on keywords have weaknesses of synonym ambiguity, lag times and shallow semantics. The paper presents a new pipeline, which adds hierarchical concepts of the Medical Subject Headings (MeSH) ontology to author keywords and then uses Latent Dirichlet Allocation (LDA) to identify topics. A data set of 624,555 journal articles from 2015 to 2024 has been collected from the Scopus API in medicine, health professions and molecular biology. Four different systematic representations, raw keywords, MeSH parent categories, direct MeSH matches, and fully enriched keywords are pre-processed. The results showed that using fully enriched keywords with MeSH ontology annotations significantly enhanced topic coherence from 0.338 (baseline keywords) to 0.574, producing more interpretable and semantically consistent topics. The spatial analysis demonstrates another evidence of changing research priorities at the geographical level. The suggested framework illustrates the strength of ontology-driven modelling and visual analytics to assist in making business-intelligence decisions in rapidly evolving biomedical settings.