Enhancing Pharmaceutical Research Through Ontology-Driven Semantic Search And Real-World Evidence Integration

Abstract

The pharmaceutical research is highly affected by the lack of data sources that are fragmented, inconsistent metadata, and inefficient search systems based on keywords. To overcome these shortcomings, this research presents PharmaSeek+, an Ontology-Driven Semantic Search Framework aimed at transforming the way researchers’ access, interpret and use real-world pharmaceutical evidence. PharmaSeek+ is designed using a systematic four step process: (1) metadata harmonization to harmonize the data of various clinical trials, drug databases, and pharmacovigilance systems by schema mapping and metadata alignment. (2) ontology development is applied by a pharmaceutical ontology that combines existing vocabularies (MeSH, SNOMED CT) with custom classes representing drug efficacy, interactions, molecular profiles, and adverse effects. (3) in semantic annotation via deep learning (DL) models, the biomedical documents are enhanced with the context-aware deep learning models such as BioBERT and contextual Natural Language Processing (NLP) in accordance with the ontology. (4) transformer-based sequence-to-query model handles the user queries which are converted into semantic queries and are SPARQL-based to support reasoning over the implicit knowledge and produce very relevant and contextual results. The PharmaSeek+ achieved 89% in search precision and 87% in recall compared to traditional search engines, and these results are confirmed using real-world pharmaceutical corpora. The findings validate the fact that PharmaSeek+ is a powerful tool that enhances the process of discovering and integrating important drug-related information. This smart structure allows quicker, more knowledgeable decision-making in pharmaceutical research and development, eventually closing the gap between fragmented biomedical information and researcher intention.