In recent years, the alignment of Artificial Intelligence technologies with people’s behaviors and worldviews has become a central topic for several sectors of Computer Science. The pervasive diffusion of Large Language Models (LLM) inside and outside the academic sector requires important efforts to ensure fairness and representativity towards all social and cultural groups, potentially considering different identities that characterize potential end-users of these technologies.

This special issue welcomes contributions on the development of graph-based abstractions and implementations of graph-based approaches for human-centered AI. It welcomes hybrid neuro-symbolic and graph-based approaches focused on knowledge reasoning for learning, and learning approaches for reasoning, as well as the design and curation of graph-based data and semantic models to explore the inclusion and representation of human identities in AI systems.

Scope

This special issue solicits submissions of research, resource and survey articles that conform to the scope of TGDK on the following specific topics:

• Ontology modeling and knowledge representation for Human-Centric AI
  • Knowledge representation for reducing bias in AI
  • Ontologies of identity dimensions and psychology for AI
  • Ontologies of sociological and communication theories for AI
  • Linked Data approaches for Human-Centric AI
• Data quality, integration and provenance for Human-Centric AI
  • FAIR and CARE principles for AI models
  • Graph-based provenance approaches for AI models
  • Incorporating cultural metadata into AI workflows
  • KG-driven approaches for bias detection and mitigation in archives
• LLM integration with graph-structured knowledge for the design of fair AI technologies
  • Question answering with LLMs and graph-structured knowledge
  • Reducing LLM hallucinations with graph-structured knowledge
  • Injecting graph-structured knowledge into LLMs
  • Retrieval-Augmented Generation using graph-structured knowledge
  • Enhancing graph-structured knowledge using LLMs
• Logic and reasoning for Explainable AI
  • Logic-based methods for governance of AI
  • Logic-based methods for ethical AI frameworks
  • Logic-based methods for legal compliance of AI
  • Extraction of logic-based representations for explainable AI
  • Graph-based constraint languages for explainable AI

Guest Editors

• Stefano De Giorgis, Vrije Universiteit Amsterdam, Netherlands
• Marco Antonio Stranisci, University of Turin, Italy
• Luana Bulla, University of Bologna, Italy
• Lia Draetta, University of Turin, Italy
• Rossana Damiano, University of Turin, Italy
• Filip Ilievski, Vrije Universiteit Amsterdam, Netherlands

Timeline

• Submissions: June 30, 2026
• Author Notifications: September 30, 2026
• Revisions: October 31, 2026
• Author Notifications: November 30, 2026
• Publication: Q4 2026

Submission

Please follow the the submission instructions for TGDK and select the corresponding Special Issue.

As a Diamond Open Access journal, official versions of accepted papers (as accessible via DOI) are published and made available for free online without fees for authors nor readers.