Details
Original language | English |
---|---|
Article number | 100856 |
Number of pages | 8 |
Journal | Journal of Web Semantics |
Volume | 84 |
Early online date | 27 Dec 2024 |
Publication status | Published - Jan 2025 |
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.
Keywords
- Counterfactual prediction, KG-based applications, Knowledge Graphs, Neuro-symbolic systems, Semantic Data Management, Valid link prediction
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Computer Networks and Communications
Sustainable Development Goals
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In: Journal of Web Semantics, Vol. 84, 100856, 01.2025.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Integrating Knowledge Graphs with Symbolic AI
T2 - The Path to Interpretable Hybrid AI Systems in Medicine
AU - Vidal, Maria Esther
AU - Chudasama, Yashrajsinh
AU - Huang, Hao
AU - Purohit, Disha
AU - Torrente, Maria
N1 - Publisher Copyright: © 2024
PY - 2025/1
Y1 - 2025/1
N2 - 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.
AB - 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.
KW - Counterfactual prediction
KW - KG-based applications
KW - Knowledge Graphs
KW - Neuro-symbolic systems
KW - Semantic Data Management
KW - Valid link prediction
UR - http://www.scopus.com/inward/record.url?scp=85213232059&partnerID=8YFLogxK
U2 - 10.1016/j.websem.2024.100856
DO - 10.1016/j.websem.2024.100856
M3 - Article
AN - SCOPUS:85213232059
VL - 84
JO - Journal of Web Semantics
JF - Journal of Web Semantics
SN - 1570-8268
M1 - 100856
ER -