Events and news relevant to the Laboratory for Foundations of Computer Science

[Roberto Barile <r.barile17@phd.uniba.it>]

*** Apologies for cross-postings ***

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XAI+KG@ISWC2026 - CALL FOR PAPERS

1st International Workshop on Explainable AI and Knowledge Graphs (XAI+KG) co-located with ISWC 2026 (https://iswc2026.semanticweb.org/) - Bari, Italy - October 25-29, 2026

Web: https://sites.google.com/view/xaikg2026

Submission: https://easychair.org/conferences/?conf=xaikg2026

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IMPORTANT DATES

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Paper Submission Deadline: July 22, 2026
Paper Notification: August 21, 2026
Camera ready: September 22, 2026

Workshop:  October 25-29, 2026 (EXACT DAY TO BE CONFIRMED)

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SCOPE

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In recent years, the synergy between eXplainable AI (XAI) and Knowledge Graphs (KGs) has gained momentum as an essential approach for achieving trust and improving understanding in AI systems.  KGs provide structured and interconnected representations of (domain-specific) knowledge, XAI aims to provide insight for predicted results and/or to clarify how numeric machine/deep learning models function internally. By leveraging KGs within XAI, research can enhance the understanding and interpretability of AI models, making it easier for users to trust AI predictions. KGs can offer insights into the relationships and logic that AI systems use to reach conclusions. This can be particularly valuable in fields requiring high levels of transparency, such as healthcare, finance, and law, where understanding the rationale behind AI predictions and actions is crucial. Conversely, XAI can assist in constructing and refining KGs, helping to identify which aspects of KGs contribute most to accurate, reliable suggestions/conclusions. This workshop aims to bring together researchers, practitioners, and industry experts to explore challenges and opportunities of combining XAI with KGs, discussing novel methodologies, applications, and case studies. Contributions will span theoretical advances, practical tools, and industry applications. 

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TOPICS

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Topics of interest for submission include, but are not limited to:

• Techniques and best practices for building interpretable models using KGs.
• Graph-Based Interpretability in Neural Networks
• Leveraging KGs to infer causality and improve explanations in AI.
• Generating human-understandable explanations from KGs for NLP applications.
• Evaluation protocols, metrics and benchmarks for assessing the quality and clarity of KG-based explanations.
• User-Centric Explanations via KGs
• Explainability in Dynamic and Temporal Knowledge Graphs
• Case studies on using XAI with KGs in Industry (e.g. healthcare, finance, and more).
• Bias and Fairness in Explainable Knowledge Graphs.
• Hands-on tools and open-source libraries for implementing XAI and KGs in real-world settings.

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SUBMISSIONS

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Submissions must be written in English, prepared using the new CEUR-ART 1-column style (which you can download here, also available as an Overleaf template here), formatted in PDF, and submitted through EasyChair workshop page (https://easychair.org/conferences/?conf=xaikg2026). XAI+KG@ISWC2026 invites submissions of research, industry, and application contributions. There are two submission formats:

• Regular papers (max 12 pages excluding references): new methods/prospective applications/new resources;
• Short papers (max 6 pages excluding references): positioning view, preliminary results/ideas/solutions falling in the workshop topics 

Submissions will undergo a single-blind review process, with each submission reviewed by 2-3 PC members. All accepted papers are expected to be presented at the conference and at least one author of each accepted paper must travel to the ISWC venue in person.

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PROCEEDINGS AND POST-PROCEEDINGS

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Accepted papers will be published as CEUR Workshop Proceedings. Additionally, authors of (selected) accepted papers will be invited to submit an extended version to a Special Issue on Explainable Neurosymbolic AI (X-NeSy) in the Neurosymbolic Artificial Intelligence journal (https://neurosymbolic-ai-journal.com/content/call-papers-special-issue-explainable-neurosymbolic-ai-x-nesy).

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ORGANIZATION

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Roberto Barile, Università degli Studi di Bari, Italy
Roberto Confalonieri, Università degli Studi di Padova, Italy 
Claudia d'Amato, Università degli Studi di Bari, Italy
Ernesto Jimenez-Ruiz, University of London, UK