[CfP] TGDK Special Issue: Neuro-Symbolic Modeling for Human-Centric AI — Submission Deadline: June 30, 2026
Stefano De Giorgis
Dear Colleagues,
We are pleased to invite you to submit your work to the Special Issue on Neuro-Symbolic Modeling for Human-Centric AI, published in the Transactions on Graph Data and Knowledge (TGDK) — a Diamond Open Access journal by Dagstuhl Publishing (free for both authors and readers).
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Motivation
The alignment of AI technologies with people's behaviors and worldviews has become a central challenge across many sectors of Computer Science. The pervasive diffusion of Large Language Models (LLMs) 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 hybrid neuro-symbolic approaches for human-centered AI.
Topics of Interest
We solicit research, resource, and survey articles aligned with the scope of TGDK, covering (but not limited to):
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 fair AI
- Question answering with LLMs and graph-structured knowledge
- Reducing LLM hallucinations with graph-structured knowledge
- Retrieval-Augmented Generation using graph-structured knowledge
- Enhancing graph-structured knowledge using LLMs
Logic and reasoning for Explainable AI
- Logic-based methods for governance, ethical frameworks, and legal compliance of AI
- Extraction of logic-based representations for explainable AI
- Graph-based constraint languages for explainable AI
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Extended Versions of Conference Papers
We explicitly welcome extended versions of previously published conference papers. If you have recently presented relevant work at a conference (e.g., NeSy, ISWC, ESWC, AAAI, IJCAI, ACL or similar venues), we strongly encourage you to consider submitting an extended version to this Special Issue.
Extended versions must:
- Clearly state in the introduction that the submission is an extension of a prior conference paper, with an explicit reference to it;
- Clarify the novel contributions presented in the extension;
- Include a significant additional contribution — such as new experiments providing stronger evidence for existing claims, new theoretical results (theorems, full proofs), or substantial new developments validating new claims.
There is no fixed minimum percentage of new content, but the extension should represent a meaningful scientific advance beyond the conference version. Authors should also ensure compliance with any copyright agreements signed with the original venue — in particular, text from papers for which copyright has been transferred to another publisher should not be reused verbatim (though scientific content can, of course, be built upon).
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Submission Types
- Research Articles
- Survey Articles
- Resource Articles (benchmarks, datasets, ontologies, tools, knowledge graphs, etc.)
Expected length: 10–20 pages using the TGDK single-column LaTeX template.
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Important Dates
- Submission deadline: June 30, 2026
- Author notification: September 30, 2026
- Revisions: October 31, 2026
- Final notification: November 30, 2026
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Submission
Please follow the TGDK submission instructions and select this Special Issue in the submission portal:
https://journal-submission.dagstuhl.de/TGDK/
Full call details are available at:
https://drops.dagstuhl.de/entities/journal/TGDK#cfp-si-neuro-symbolic-modeling-for-human-centric-ai
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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
We look forward to your contributions!
Best regards,
The Guest Editors