Xuteng Luo

Abstract As artificial intelligence (AI) continues to advance and scale up in biomedical research, concerns about AI's trustworthiness and transparency have grown. There is a critical need to systematically bring accurate and relevant biomedical knowledge into AI applications for transparency and provenance. Knowledge graphs have emerged as a powerful tool that integrates heterogeneous knowledge by explicitly describing biomedical knowledge as entities and relationships between entities.

Abstract Knowledge graphs have recently emerged as a powerful data structure to organize biomedical knowledge with explicit representation of nodes and edges. The knowledge representation is in a machine-learning ready format and supports explainable AI models. However, PubMed, the largest and richest biomedical knowledge repository, exists as free text, limiting its utility for advanced machine learning tasks.