Integrating Knowledge Graphs with Symbolic AI

The Path to Interpretable Hybrid AI Systems in Medicine

authored by: 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.
Organisation(s): Institute of Data Science
L3S Research Centre
External Organisation(s): German National Library of Science and Technology (TIB)
Universidad Autónoma de Madrid
Type: Article
Journal: Journal of Web Semantics
Volume: 84
No. of pages: 8
ISSN: 1570-8268
Publication date: 01.2025
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Software, Human-Computer Interaction, Computer Networks and Communications
Sustainable Development Goals: SDG 3 - Good Health and Well-being
Electronic version(s): https://doi.org/10.1016/j.websem.2024.100856 (Access: Open)