2ndKG-STAR@ESWC2025: Knowledge Graphs for Responsible AI @ESWC2025 Portoroz, Slovenia, June 1-5, 2025 |
| Conference website | https://sites.google.com/view/kg-star/home |
| Submission link | https://easychair.org/conferences/?conf=2ndkgstareswc2025 |
| Submission deadline | March 6, 2025 |
Call for Papers
Knowledge Graphs for Responsible AI (KG-STAR)Workshop in conjunction with ESWC25June 1, 2025
Responsible AI prioritizes principles such as fairness, transparency, accountability, and inclusivity in the development and deployment of AI systems. With the rapid evolution of AI technologies, including Generative AI, addressing ethical challenges and societal impacts has become crucial. Knowledge Graphs (KGs) provide a structured representation of information, enabling improved generative AI performance, reducing biases, and fostering explainability. By leveraging semantic relationships and contextual understanding, KGs promote transparent decision-making and facilitate the development of fair, interpretable, and inclusive AI systems.
This workshop aims to explore how Knowledge Graphs can be leveraged to advance Responsible AI principles. Building on the success of the first edition at CIKM 2024, KG-STAR provides a collaborative platform for researchers, practitioners, and policymakers to share insights, methods, and applications of Knowledge Graphs in enabling Responsible AI.
- Submission Deadline: March 6, 2025
- Notifications: April 3, 2025
- Camera-Ready Version: April 17, 2025
- Workshop Date: June 1, 2025
We invite submissions of original research, case studies, and position papers related to Knowledge Graphs and their role in promoting Responsible AI. Topics of interest include, but are not limited to:
Knowledge Graphs for Bias Mitigation:
● Techniques and methodologies for using Knowledge Graphs to identify and mitigate biases in AI models.
● Case studies demonstrating the successful application of Knowledge Graphs in addressing bias challenges.
Interpretability and Explainability:
● Approaches to enhancing the interpretability and explainability of black-box AI models through integrating
Knowledge Graphs.
● Evaluating the effectiveness of Knowledge Graphs in making AI decision-making processes more transparent.
Privacy-Preserving Knowledge Graphs:
● Methods for constructing Knowledge Graphs that prioritize privacy and comply with data protection
regulations.
● Applications of Knowledge Graphs in privacy-preserving AI systems.
Fairness in AI with Knowledge Graphs:
● How Knowledge Graphs contribute to ensuring fairness in AI applications.
● Techniques for using Knowledge Graphs and their embeddings to identify and rectify unfair biases in AI
models.
Ethical Considerations in Knowledge Graph Construction:
● Ethical challenges in the creation and maintenance of Knowledge Graphs.
● Best practices for ensuring responsible and ethical Knowledge Graph development.
● Real-world applications of Knowledge Graphs in Responsible AI.
Integration of Large Language Models (LLMs) and Knowledge Graphs (KGs):
● Enhancing LLMs’ accuracy, consistency, reducing hallucinations and harmful content generation, fake news
detection, fact checking, etc. with knowledge-grounded techniques, e.g., Grapgh RAG (graph-based retrieval
augmented generation) and KG RAG.
● Enhancing the interoperability of KG downstream tasks through LLMs’ natural language interfaces,
transferability, and generalization capacity, e.g., GNN (graph neural network)-LLM alignment.
- All submissions must be original, unpublished, and written in English.
- Papers should follow the ESWC formatting guidelines and should not exceed 8 pages (including references).
- Submissions will be peer-reviewed based on originality, quality, relevance, and clarity.
- Accepted papers will be included in the workshop proceedings and shared with the ESWC25 community.
- Submission link: https://easychair.org/my/conference?conf=2ndkgstareswc2025
For further details and submission instructions, visit the workshop website : https://sites.google.com/view/kg-star/home.
For inquiries, please email ahmed.zalouk@mail.bcu.ac.uk .
We look forward to your submissions and contributions to advancing Responsible AI through Knowledge Graphs!