Enhancing Medical Knowledge Discovery

A Neuro-symbolic System for Inductive Learning over Medical KGs

authored by

Disha Purohit, Yashrajsinh Chudasama, Maria Esther Vidal

Abstract

Medical knowledge graphs (KGs) excel at integrating heterogeneous healthcare data with domain knowledge, but face challenges due to incompleteness. While Knowledge Graph Embedding (KGE) models show promise in link prediction, they often fail to incorporate crucial semantic constraints from medical ontologies and clinical guidelines. We propose a neuro-symbolic system that enhances medical knowledge discovery by combining symbolic learning from medical ontologies, inductive learning through KGE, and semantic constraint validation. Applied to lung cancer care, our system demonstrates enhanced performance in predicting novel medical relationships while maintaining semantic consistency with medical knowledge. Experimental results show our approach enhances the KGE model's performance while ensuring clinical validity and the implementation is publicly accessible on GitHub.

Organisation(s)

L3S Research Centre

External Organisation(s)

German National Library of Science and Technology (TIB)

Type

Conference contribution

Pages

1108-1109

No. of pages

2

Publication date

10.03.2025

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computer Networks and Communications, Computer Science Applications, Software

Sustainable Development Goals

SDG 3 - Good Health and Well-being

Electronic version(s)

https://doi.org/10.1145/3701551.3708814 (Access: Closed)