Biomedical Ontology Matching using Relational Graph Neural Networks and RDFs Meta-Path Rules

The growing complexity of relational data in knowledge graphs necessitates advanced models to capture intricate graph structures. In the domain of health and life sciences, the use of biomedical ontologies prevails in many applications from database management to retrieval and publication. Due to heterogeneity and lack of standardization to create local ontologies, the reusibility and interoperability for these resources become limited whereby often manual and time-consuming processes are put in place to match representations for cross-domain applications. In this paper we explore embedding-based methods as an alternative approach for entity matching among biomedical ontologies at different complexity and interoperability levels and propose a novel framework base on Relational Graph Convolutional Networks (R-GCN) in combination with symbolic meta-rule integration. We compare our results to the state-of-the-art baseline models using metrics such as Hits@k, F-scores and Mean Rank (MR) and demonstrate the effectiveness of the proposed model in improving ontology matching tasks across multiple complex biomedical datasets.