Abstract:
Biomedical Relation Extraction (RE) systems classify biomedical entities to improve our understanding of biological and medical processes. Most cutting-edge systems use deep learning to find relationships between similar entities like proteins or drugs. Ontologies, which formalize and integrate biomedical information in direct acyclic graphs, are ignored by these systems, which focus on textual identification. However, Knowledge Graph (KG)-based recommendation systems demonstrated the value of integrating KGs to enhance items. Users watch or read movies or books and rate them. This study proposes integrating KGs into biomedical RE using a recommendation model to expand their scope. We created K-BiOnt by integrating a baseline deep biomedical RE system with an existing KG-based recommendation system. Adding KG-based recommendations improves the system’s ability to identify true relations that the baseline deep RE model couldn’t extract from the text.
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