Enhancing Medical Knowledge Discovery

A Neuro-symbolic System for Inductive Learning over Medical KGs

verfasst von

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.

Organisationseinheit(en)

Forschungszentrum L3S

Externe Organisation(en)

Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek

Typ

Aufsatz in Konferenzband

Seiten

1108-1109

Anzahl der Seiten

2

Publikationsdatum

10.03.2025

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Computernetzwerke und -kommunikation, Angewandte Informatik, Software

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1145/3701551.3708814 (Zugang: Geschlossen)