KG-STAR 2025: 2nd International Workshop on Knowledge Graphs for Responsible AI
co-located with the 22nd Extended Semantic Web Conference (ESWC)
June 1 – 5, 2025 | Portoroz | Slovenia.
🌍 Join us at ESWC 2025 as we explore the intersection of Knowledge Graphs (KGs) and Responsible AI. We invite high-quality submissions that address key challenges and opportunities in this space.
🔍 Topics of Interest (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, and consistency, reducing hallucinations and harmful content generation, fake news detection, fact-checking, etc., with knowledge-grounded techniques, e.g., Graph 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.
👥 Organizing Committee:
👩💻 Edlira Kalemi Vakaj, Birmingham City University, UK
🧑💻 Nandana Mihindukulasooriya, IBM Research, USA
🧑💻 Manas Gaur, University of Maryland Baltimore County, USA
🧑💻 Arijit Khan, Aalborg University, Denmark