Call for Workshop Papers
IJCKG 2026 cordially invites you to submit papers to the workshops to be held in conjunction with the conference in Bangkok, Thailand.
Two workshops, held in conjunction with IJCKG 2026.
How to submit your workshop paper.
Published online via CEUR-WS.org.
Accepted papers of the Workshops, Posters, Demos, and Challenges Tracks will be submitted to CEUR-WS.org for online publication.
RDM-KG 2026: Workshop on Research Data Management for Knowledge Graphs
Abstract
The volume and complexity of research data continue to grow rapidly across scientific domains. Effective Research Data Management is now a prerequisite for reproducibility, collaboration, and compliance with FAIR (Findable, Accessible, Interoperable, Reusable) data principles mandated by funding agencies worldwide. Knowledge graphs offer a powerful paradigm for organising, connecting, and querying heterogeneous research data, enabling richer metadata representations, provenance tracking, and cross-disciplinary integration.
RDM-KG 2026 brings together the RDM and Knowledge Graph communities to address shared challenges, including data interoperability, ontology design, and data lifecycle management.
Topics of Interest
- FAIR data principles: frameworks, implementation, and assessment
- Data lifecycle management: acquisition, curation, archival, and preservation
- Data management plans (DMPs) and institutional policies
- Research data repositories: design, interoperability, and evaluation
- Metadata standards and schemas for scientific datasets
- Provenance modelling and reproducibility in research workflows
- Data governance, access control, and licensing
- Knowledge graph construction and population from research datasets
- Ontologies and vocabularies for research data description
- Linked Data and semantic integration of heterogeneous data sources
- SPARQL and graph query languages for research data retrieval
- Named entity recognition and information extraction for scientific texts
- Federated knowledge graphs and cross-institutional data sharing
- Large language models (LLMs) for metadata generation and data curation
- Machine learning for data quality assessment and anomaly detection
- Automated knowledge graph alignment and schema matching
- Neuro-symbolic approaches for research data reasoning
- Data catalogues, knowledge portals, and discovery services
- Biomedical and health research data management
- Environmental and earth sciences data integration
- Humanities and social sciences digital data
- Open government and public sector research datasets
- Data management in multi-institutional collaborative projects
Workshop Organizers
KGE-LLM 2026: International Workshop on Knowledge Graph Engineering in the Era of Large Language Models
Abstract
This workshop focuses on the bidirectional synergy between LLMs for Knowledge Graph Engineering and Knowledge Graphs for Large Language Models. It aims to provide a forum for researchers and practitioners working at the intersection of these two rapidly evolving research areas.
Large Language Models (LLMs) are transforming the way Knowledge Graphs (KGs) and ontologies are constructed, maintained, and utilized. LLMs offer new opportunities for knowledge acquisition, knowledge graph and ontology engineering, entity and relation extraction, semantic annotation, knowledge graph completion, and natural language interaction with structured knowledge. At the same time, Knowledge Graphs are becoming an essential foundation for enhancing the reliability, explainability, controllability, and factual grounding of LLMs and LLM-based AI systems through Graph Retrieval-Augmented Generation (GraphRAG), knowledge-grounded reasoning, semantic memory, and agentic AI.
We welcome contributions covering both directions of research: applying LLMs to knowledge graph and ontology engineering, and leveraging Knowledge Graphs to enhance LLMs and LLM-based AI systems. The workshop also encourages discussions on evaluation methodologies, trustworthy AI, benchmark datasets, practical deployment, and real-world applications.
By bringing together researchers from the Knowledge Graph, Semantic Web, Ontology Engineering, Natural Language Processing, and AI communities, the workshop seeks to foster interdisciplinary discussion on how Knowledge Graph Engineering should evolve in the era of Large Language Models.
Topics of Interest
Topics include, but are not limited to:
- LLMs for Knowledge Graph Engineering
- Knowledge Graphs for Large Language Models
- Graph Retrieval-Augmented Generation (GraphRAG)
- Knowledge-grounded reasoning and AI agents
- Hybrid neuro-symbolic AI
- Evaluation and trustworthy LLM-KG systems
- Tools, platforms, and benchmark datasets
- Applications of LLM-KG technologies
Workshop Organizers
Kouji Kozaki is a Professor in the Department of Engineering Informatics at Osaka Electro-Communication University, Japan. His research interests include Knowledge Graphs, Ontology Engineering, Semantic Web technologies, Large Language Models, and knowledge-based AI systems. He has served as Local Organizing Chair of ISWC 2025, Program Co-Chair of IJCKG 2026, and Senior Program Committee member for several international conferences in the areas of Knowledge Graphs and Semantic Web.
Takeshi Morita is a Professor in the College of Science and Engineering at Aoyama Gakuin University, Japan. His research interests include Knowledge Graphs, Ontology Learning, and systems integrating Semantic Web Technologies with Large Language Models. He has served as Publicity Co-Chair for JIST 2018 and IJCKG 2023, as Demo & In-Use Track Co-Chair for IJCKG 2026, and has also served on the program committees of several international conferences in the fields of Knowledge Graphs and Semantic Technologies.
Takanori Ugai received his PhD degree in Engineering from Tokyo Institute of Technology, Japan, in 2013. He has been a researcher at Fujitsu Limited since 1992. He is also a researcher at the National Institute of Advanced Industrial Science and Technology (AIST), Japan, and a lecturer at the University of Tsukuba, Japan. His research interests include Knowledge Graphs, Graph Neural Networks, and Requirements Engineering. He has served as a Program Committee member of ISWC since 2019.
Nguyen Duy Hung received his PhD degree from the Asian Institute of Technology, Thailand, in 2013 and is currently an Associate Professor at Sirindhorn International Institute of Technology, Thammasat University. His research interests include computational argumentation, defeasible and probabilistic reasoning, decision making, negotiation, legal reasoning, conflict resolution, dialogues, and argumentative explainable AI.