Application of Knowledge Graphs

Dear Colleagues,

In recent years, knowledge graphs have emerged as a practical and powerful way to organise and connect information. Whether it is linking patient records, improving search experiences, or supporting recommendation systems, they help us make sense of complex data by highlighting the relationships that matter.

This Special Issue focuses on how knowledge graphs are being applied in real-world settings, and how they are evolving alongside technologies like Large Language Models (LLMs) and explainable AI (xAI). As LLMs reshape how we interact with systems through natural language, knowledge graphs provide essential context and structure, grounding these models in factual and meaningful representations. At the same time, xAI is opening the door to more transparent and trustworthy AI, especially when combined with the relational insights that graphs can offer.

We are particularly interested in work that brings these areas together: new methods, case studies, or systems that explore the interaction between graphs, language models, and explainability. We also welcome contributions that tackle the practical aspects of building and maintaining knowledge graphs: from semi-automated construction and enrichment to handling large-scale or constantly evolving data.

By bringing together diverse perspectives from research and industry, this Special Issue aims to offer both a grounded view of where knowledge graphs stand today and a glimpse into where they are heading next.

Topics of Interest

• Applications of Knowledge Graphs in Healthcare, Education, Cybersecurity, e-Government, Smart Cities, Energy, etc.
• Large Language Models (LLMs) and Knowledge Graph Integration.
• Techniques for Knowledge Graph Construction and Maintenance.
• Explainable AI (xAI) and Knowledge Graphs for Transparent Decision-Making.
• Enhancing Human-Assistive Systems Through Graph-based Knowledge
• Knowledge-driven Evaluation of xAI Systems.
• Semantic Search and Recommendation Systems Powered by Knowledge Graphs.
• Scalability and Dynamic Data Integration in Knowledge Graphs.
• Advances in Knowledge Graph Representation and Reasoning.
• Case Studies on Real-World Deployments of Knowledge Graphs.
• Interdisciplinary Approaches to Knowledge Graphs in AI Systems.
• Evaluation and Benchmarking of Knowledge Graph-Driven Frameworks.
• Performance, Usability, and Impact of KG-based AI solutions.
• Ethical Considerations and Trustworthiness in Knowledge Graph-Based AI Solutions.

Dr. Georgios Meditskos
Dr. Maria Papoutsoglou
Dr. Stefanos Vrochidis
Prof. Dr. Nick Bassiliades
Guest Editors

Manuscript Submission Information

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• knowledge graphs
• large language models (LLMs)
• explainable AI (xAI)
• semantic relationships
• data integration
• natural language processing
• contextual understanding
• graph construction
• data enrichment
• trustworthy AI