Construction of a Biomedical Knowledge Graph using Natural Language Processing

Biomedical Named Entity Linking (NEL) plays a crucial role in extracting valuable information from text for downstream tasks in bioinformatics. However, challenges like entity ambiguity and complex relationships hinder accurate entity linking. We demonstrate the effectiveness of BERN2, a novel neural network architecture designed to address these challenges and improve NEL performance, by linking entities to a knowledge graph stored in a Neo4j graph database. This combination allows for efficient exploration of intricate relationships between entities, facilitating advanced biomedical research. Furthermore, we showcase the utility of leveraging node embedding techniques on the knowledge graph for various machine-learning applications in the biomedical domain. Our findings suggest that BERN2, coupled with knowledge graphs and node embeddings, can significantly enhance biomedical information extraction and empower researchers with powerful tools for collaborative healthcare research and accelerated discoveries.