Desiderata for a biomedical knowledge network: opportunities, challenges and future Directions

Area of Science:

• Biomedical Informatics
• Knowledge Representation
• Artificial Intelligence

Background:

• Knowledge graphs are vital for knowledge discovery in complex biomedical systems.
• Biomedical data complexity necessitates dynamic reasoning, abstraction, and standardization in knowledge graphs.

Purpose of the Study:

• To outline key desiderata for developing and utilizing biomedical knowledge graphs.
• To address challenges and define future directions for a biomedical knowledge network.

Main Methods:

• Synthesized insights from a meeting of leading scientists.
• Identified six critical areas for advancing biomedical knowledge graphs.

Main Results:

• Domain-centric reasoning is needed for biomedical knowledge graphs.
• Harmonized standards, robust validation, and integration with large language models are essential.
• Secure sharing frameworks and ethical governance are critical for trustworthy knowledge graphs.

Conclusions:

• Addressing these six desiderata will enable trustworthy and actionable biomedical knowledge discovery.
• Realizing the potential of a biomedical knowledge network is crucial for advancing the field of biomedicine.