Integrating Knowledge Graphs with Symbolic AI

The Path to Interpretable Hybrid AI Systems in Medicine

verfasst von

Maria Esther Vidal, Yashrajsinh Chudasama, Hao Huang, Disha Purohit, Maria Torrente

Abstract

Knowledge Graphs (KGs) are graph-based structures that integrate heterogeneous data, capture domain knowledge, and enable explainable AI through symbolic reasoning. This position paper examines the challenges and research opportunities in integrating KGs with neuro-symbolic AI, highlighting their potential to enhance explainability, scalability, and context-aware reasoning in hybrid AI systems. Using a lung cancer use case, we illustrate how hybrid approaches address tasks such as link prediction—uncovering hidden relationships in medical data—and counterfactual reasoning—analyzing alternative scenarios to understand causal factors. The discussion is framed around TrustKG, which demonstrates how constraint validation, causal reasoning, and user-centric communication can support transparent and reliable decision-making. Additionally, we identify current limitations of KGs, including gaps in knowledge coverage, evolving data integration challenges, and the need for improved usability and impact assessment. These insights are not limited to healthcare but extend to other domains like energy, manufacturing, and mobility, showcasing the broad applicability of KGs. Finally, we propose research directions to unlock their full potential in building robust, transparent, and widely adopted real-world applications.

Organisationseinheit(en)

Institut für Data Science
Forschungszentrum L3S

Externe Organisation(en)

Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
Universidad Autónoma de Madrid (UAM)

Typ

Artikel

Journal

Journal of Web Semantics

Band

84

Anzahl der Seiten

8

ISSN

1570-8268

Publikationsdatum

01.2025

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Software, Mensch-Maschine-Interaktion, Computernetzwerke und -kommunikation

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1016/j.websem.2024.100856 (Zugang: Offen)