Enhancing Ontology Integration in Medical Texts through Advanced Mechanisms

Abstract

Abstract Ontology integration involves combining several data sources into a single and unified knowledge representation. In the context of medical texts, ontology integration plays a crucial role, as these texts include essential knowledge for clinical and research applications. However, existing methods for incorporating ontologies into medical texts have exhibited limitations in terms of comprehensiveness, flexibility, semantic accuracy, logical rigor, efficiency, and overall effectiveness. To address these shortcomings, this paper introduces an enhanced ontology integration mechanism tailored specifically for medical texts. Our proposed mechanism integrates various data sources within medical texts seamlessly by employing a combination of ontological, logical, lexical, structural, semantic, declarative, and machine-learning techniques. This mechanism leverages a common upper ontology and a set of transformation rules to align concepts and relationships across different source ontologies. Additionally, it utilizes a supervised machine learning approach to predict mappings between concepts and relationships originating from various source ontologies. To assess the effectiveness of our mechanism, extensive evaluations using diverse datasets, ontologies, and benchmark tests for ontology matching, evaluation, text processing, and application were undertaken. Through rigorous comparisons with existing approaches, the superior performance and effectiveness of our mechanism were demonstrated. In addition, a comprehensive analysis of its strengths, weaknesses, and implications for both research and real-world implementation within the field of medicine was also performed. This mechanism represents a significant advancement in ontology integration for medical texts, empowering data-driven decision-making in the field of medicine.