Knowledge Management and Semantic Web Technologies for Explainable Artificial Intelligence

Dear Colleagues,

At this point, considering that data are ubiquitous, their efficient is crucial. The convergence of knowledge management (KM), semantic web technologies (SWT), and explainable artificial intelligence (XAI) presents fertile ground for both research and practical applications. This Special Issue aims to address the latest advancements, challenges, and opportunities in leveraging semantic web technologies to improve knowledge management processes by enhancing decision-making, fostering innovation, and encouraging user trust in AI systems.

Topics of interest include, but are not limited to, the following:

• Semantic web technologies for KM and XAI: Ontologies, linked data, RDF, OWL, SPARQL, and other semantic technologies with which to increase our ability to explain the abilities of AI.
• Integration of KM and semantic web: Case studies and frameworks demonstrating how semantic web technologies can be integrated with KM practices to boost transparency and decision-making effectiveness.
• Semantic search and retrieval: Techniques for improving the search for and retrieval of knowledge assets using semantic technologies, focusing on the clarity and interpretability of results.
• Knowledge discovery and data mining: Using semantic web technologies for knowledge discovery and data mining in large or heterogeneous data sources, enhancing the explainability of the derived insights.
• Intelligent KM systems: Development of intelligent systems that leverage AI, machine learning, and semantic technologies, focusing on systems that provide clear explanations of their reasoning processes.
• Integration of semantic web technologies with XAI for enhanced knowledge representation and reasoning in KM.
  • Knowledge management frameworks leveraging XAI for better decision support.
  • Semantic reasoning and inference mechanisms to improve AI explainability in KM systems.
• Applications and case studies: Practical applications and case studies showcasing the implementation of semantic web technologies in KM across various domains, with an emphasis on systems that offer clear and understandable outcomes.
• Interoperability and standards: Standards and frameworks that ensure interoperability between KM systems and semantic web technologies for a unified approach to transparent and interpretable AI.
• Challenges and future directions: Current challenges, unresolved issues, and future research directions in the integration of KM, semantic web technologies, and XAI.

Prof. Dr. Cecilia Zanni-Merk
Dr. Franco Giustozzi
Dr. Ali Ayadi
Guest Editors

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