IJCKG 2026 CFP

-----------------------------------------------------------------------

IJCKG 2026: 15th International Joint Conference on Knowledge Graphs

November 19–21, 2026, Bangkok, Thailand

https://ijckg2026.aiat.or.th

UPDATE!

- Deadline Extension!

Research / In-Use / Education Tracks
- Abstract Submission Deadline: June 23,2026 (Extended) July 17, 2026
- Full Paper Submission Deadline: June 30, 2026 (Extended) July 24, 2026
- Acceptance Notification: August 26, 2026

- Selected high-quality papers will be invited to submit extended versions to a special issue of the New Generation Computing journal (Springer), subject to the journal’s review process.

-----------------------------------------------------------------------

*** Overview ***

The 15th International Joint Conference on Knowledge Graphs (IJCKG 2026) is an academic forum on Knowledge Graphs. The mission of IJCKG 2026 is to bring together researchers in the international Knowledge Graph community and related areas to present innovative research results and novel applications of Knowledge Graphs. IJCKG has evolved from the Joint International Semantic Technology Conference (JIST), a joint event for disseminating research results regarding the Semantic Web, Knowledge Graphs, Linked Data, and AI on the Web.

IJCKG 2026 will take place in Bangkok, Thailand, hosted by the National Electronics and Computer Technology Center, Thailand and the Artificial Intelligence Association of Thailand.

*** Theme ***

The theme of this conference is “Knowledge Graphs for Agentic, Multimodal, and Retrieval-Augmented Intelligence”, which aims to explore the evolving role of knowledge graphs in next-generation AI systems. As artificial intelligence advances from standalone foundation models toward agentic, multimodal, and retrieval-augmented paradigms, knowledge graphs provide essential support for structured knowledge integration, semantic grounding, and explainable reasoning.

This theme highlights the convergence of symbolic knowledge representation and data-driven AI, including the integration of knowledge graphs with retrieval-augmented generation (RAG), graph-enhanced large language models, autonomous and agentic systems, tool use, and multimodal learning. It also encourages research on knowledge representation, ontology engineering, knowledge acquisition, reasoning, and system-level integration for intelligent applications.

The conference program will include workshops, keynotes, a frontiers and trends forum, industry forum, young scholars forum, evaluations and competitions, paper presentations, posters, and demos. We invite researchers from academia and practitioners from industry to share recent advances and practical experiences, fostering collaboration between research and application.

In addition to research and application papers, IJCKG 2026 will continue to emphasize knowledge graph open resources to support data and system sharing in academia and industry, including knowledge graphs, ontologies, datasets, tools, APIs, frameworks, and standards.

*** Tracks ***

• Research Track (Full paper, 15 pages)
• In-Use Track (Full paper, 15 pages)
• Education Track (Full paper, 15 pages)
• Poster and Demo Track (Short paper, 6-8 pages)
• Workshops Track (Full paper, 15 pages and Short paper, 6-8 pages)
• Industry Forum (TBA)
• Evaluation Challenges (TBA)

*** Details of Tracks ***

Research Track (Full Paper, 15 pages)

The Research Track solicits original and unpublished research contributions on all aspects of Knowledge Graphs and related technologies. Submissions should present significant advances in theories, methodologies, algorithms, systems, applications, or evaluations related to Knowledge Graphs. Topics include, but are not limited to, knowledge representation, ontology engineering, knowledge acquisition, reasoning, graph analytics, knowledge graph construction and maintenance, semantic technologies, and the integration of Knowledge Graphs with AI technologies such as large language models, retrieval-augmented generation, multimodal AI, and agentic systems.

In-Use Track (Full Paper, 15 pages)

The IJCKG In-Use Track provides a forum to explore the benefits and challenges of applying Knowledge Graph technologies in concrete, practical use cases, in contexts ranging from industry to government and society (e.g., cultural heritage, astrophysics, biodiversity, medicine). The track aims to give a stage to applied works addressing real-world problems in which Knowledge Graph technologies have been employed, possibly in combination with machine learning, deep learning, large language models, and other AI techniques. The In-Use Track seeks submissions describing applied research as well as software tools, systems, or architectures that benefit from the use of Knowledge Graph technologies. Importantly, submitted papers should provide convincing evidence of the use of the proposed application or tool by the target user group, preferably outside the group that conducted the development and, more broadly, outside the Knowledge Graph research community.

Education Track (Full paper, 15 pages)

The IJCKG 2026 Education Track provides a forum for researchers and practitioners to explore the role of Artificial Intelligence (AI) and Knowledge Graphs (KGs) in advancing education, learning, and educational technologies. The track aims to bring together innovative research on AI-driven learning environments, knowledge-enhanced educational systems, and emerging applications of generative AI, retrieval-augmented generation (RAG), and knowledge representation in education.
The Education Track seeks submissions describing theoretical, methodological, and applied research, as well as educational tools, systems, and infrastructures that leverage AI and Knowledge Graph technologies. Topics of interest include AI in Education, Knowledge Graphs for Learning, Generative AI and Educational Applications, Knowledge Representation and Educational Infrastructure, and Evaluation, Interaction Design, and Community Resources for Education.

Poster and Demo Track (Short Paper, 6-8 pages)

IJCKG 2026 is pleased to invite submissions to the Poster and Demo Track, which complements the full-paper tracks of the conference. This track provides an opportunity to present late-breaking research results, ongoing research projects, innovative ideas, and work in progress.
Poster and Demo presentations allow researchers to present their work directly to conference participants and receive valuable feedback on significant work in progress, cutting-edge research, emerging ideas, or systems that are best communicated through interactive or visual demonstrations.
We welcome submissions relevant to the field of Knowledge Graphs, including but not limited to the topics covered by the IJCKG 2026 full-paper tracks. Suitable submissions include reports on ongoing or completed research, software systems, idea papers that introduce promising research directions, position papers presenting a bird's-eye view of a research topic, and PhD thesis abstracts.

Workshop Track (Full paper, 15 pages; Short Paper, 6-8 pages)

- RDM-KG 2026: Workshop on Research Data Management for Knowledge Graphs

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. For further details, please visit https://ijckg2026.aiat.or.th/call-for-workshops#rdm-kg

- KGE-LLM 2026: International Workshop on Knowledge Graph Engineering in the Era of Large Language Models

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. For further details, please visit https://ijckg2026.aiat.or.th/call-for-workshops#kge-llm

Industry Forum (Full paper, 15 pages)

The IJCKG 2026 Industrial Track provides a premier forum for industry practitioners, engineers, and applied researchers to showcase how Knowledge Graph technologies and AI are creatively deployed to solve complex, real-world problems while actively contributing to human flourishing. Submissions in this track should go beyond theoretical design to describe concrete, deployed industrial use cases, emphasizing creativity, user experience, and the positive impact of these systems on people, organizations, and society.

Evaluation Challenges

TBA

*** Important Dates ***

All deadlines are 23:59 AoE (Anywhere on Earth)

• Research / In-Use / Education Tracks
  - Abstract Submission Deadline: June 23,2026 (Extended) July 17, 2026
  - Full Paper Submission Deadline: June 30, 2026 (Extended) July 24, 2026
  - Acceptance Notification: August 26, 2026
• Poster and Demo Track (1st round)
  - Full Paper Submission Deadline: August 10, 2026
  - Acceptance Notification: September 01, 2026
• Poster and Demo Track (2nd round)
  - Full Paper Submission Deadline: September 10, 2026
  - Acceptance Notification: September 25, 2026
• Industry Forum
  - Abstract Submission Deadline: July 17, 2026
  - Full Paper Submission Deadline: July 24, 2026
  - Acceptance Notification: August 26, 2026
• Workshop Track
  RDM-KG 2026
  - Workshop Paper Submission Deadline: September 9, 2026
  - Acceptance Notification: September 25, 2026
  KGE-LLM 2026
  - Workshop Paper Submission Deadline: September 9, 2026
  - Acceptance Notification: September 25, 2026
• Evaluation Challenges
  - TBA

*** Topics of interest include, but are not limited to ***

1. Knowledge Graphs for Generative AI, RAG, and Agentic Systems

• Knowledge Graphs for Retrieval-Augmented Generation, GraphRAG, and grounded generation
• Integration of Knowledge Graphs with Large Language Models
• KG4LLM and LLM4KG methods, systems, and applications
• Knowledge Graphs for autonomous, agentic, and tool-using AI systems
• Planning, reasoning, decision-making, and AI system orchestration with Knowledge Graphs
• Contextual grounding and knowledge integration using Knowledge Graphs

2. Knowledge Representation, Ontologies, and Semantic Web

• Knowledge representation and ontology engineering
• Ontology modeling, evolution, alignment, and reuse
• Semantic Web technologies and Linked Data
• Standards, vocabularies, and frameworks for Knowledge Graph development

3. Knowledge Graph Construction, Acquisition, and Integration

• Entity, relation, and event extraction for Knowledge Graphs
• Acquisition of complex knowledge, including events, rules, workflows, and processes
• Multimodal Knowledge Graph construction and integration
• Knowledge integration from structured, semi-structured, unstructured, and multimodal sources

4. Knowledge Graph Management, Querying, and Infrastructure

• Graph databases and Knowledge Graph management systems
• Graph query languages and semantic query processing
• Indexing, scalability, distributed processing, and optimization for large-scale Knowledge Graphs
• Data quality, provenance, trust, versioning, and lifecycle management of Knowledge Graphs

5. Learning, Reasoning, and Analytics over Knowledge Graphs

• Knowledge Graph embeddings and representation learning
• Knowledge base completion, link prediction, and graph inference
• Machine learning and graph neural networks on Knowledge Graphs
• Graph classification, clustering, generation, and anomaly detection
• Reasoning, rule learning, and neuro-symbolic methods for Knowledge Graphs

6. Knowledge Graph-based Retrieval, Search, and Question Answering

• Knowledge Graph-based information retrieval and semantic search
• Question answering over Knowledge Graphs
• Cross-modal retrieval and reasoning with Knowledge Graphs
• Dialogue systems and conversational AI with Knowledge Graphs

7. Knowledge Graph Applications and Intelligent Systems

• Recommendation systems and decision support using Knowledge Graphs
• Industrial and government applications of Knowledge Graphs
• Knowledge Graphs for science, education, healthcare, cultural heritage, social good, and sustainability
• Domain-specific Knowledge Graphs and intelligent applications

8. Evaluation, Interaction, and Open Resources

• Evaluation methods, benchmarks, and datasets for Knowledge Graphs, RAG, GraphRAG, and KG-enhanced AI systems
• Knowledge Graph visualization, exploration, and human–KG interaction
• Open Knowledge Graph resources, tools, platforms, and reusable datasets
• Reproducibility, benchmarking practices, and community resources

*** Submission Guidelines ***

Authors are invited to submit original, unpublished contributions via EasyChair: https://easychair.org/conferences/?conf=ijckg2026. Submissions must be written in English and formatted according to the Springer LNCS/LNAI guidelines.

Full papers should be between 12-15 pages (including references). Short papers should be between 6-8 pages (including references).

Formatting instructions and templates are available at: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines

*** Proceedings ***

Accepted papers of Research, In-Use and Education Tracks will be published in the conference proceedings by Springer in the Lecture Notes in Artificial Intelligence (LNAI) series, which is part of the Lecture Notes in Computer Science (LNCS) series.

Selected high-quality papers will be invited to submit extended versions to a special issue of the New Generation Computing journal (Springer), subject to the journal’s review process.

Accepted papers of Workshops, Posters, Demos, and Challenges Tracks will be submitted to CEUR-WS.org for online publication.

-----------------------------------------------------------------------

General Chairs

• Thepchai Supnithi (National Electronics and Computer Technology Center, Thailand; Artificial Intelligence Association of Thailand, Thailand)
• Dimitris Plexousakis (Foundation for Research and Technology - Hellas, Greece; University of Crete, Greece)

Program Chairs

• Chutiporn Anutariya (Asian Institute of Technology, Thailand)
• Kozaki Kouji (Osaka Electro-Communication University, Japan)
• Manolis Koubarakis (National and Kapodistrian University of Athens,Greece)

Local Chairs

• Taneth Ruangrajitpakorn (National Electronics and Computer Technology Center, Thailand)
• Rachasak Somyanonthanakul (Thammasat University, Thailand; Artificial Intelligence Association of Thailand, Thailand)

Workshop Chairs

• Rathachai Chawuthai (King Mongkut's Institute of Technology Ladkrabang, Thailand)
• Chuanyi Liu (Harbin Institute of Technology, Shenzhen, China)

In-Use Track Chairs

• Hutchatai Chanlekha (Kasetsart University, Thailand)
• Takeshi Morita (Aoyama Gakuin University, Japan)
• Shumin Deng (Zhejiang University, China)

Industry Track Chairs

• Monchai Lertsutthiwong (KBTG, Thailand)
• Eiichi Sunagawa (Toshiba Corp, Thailand)

Education Track Chairs

• Wirapong Chansanam (Khon Kaen University, Thailand)
• Natthawut Kertkeidkachorn (Japan Advanced Institute of Science and Technology, Japan)

Young Scholar Forum Chair

• Aslan B. Wong (Artificial Intelligence Association of Thailand, Thailand)

Evaluation Challenge Chairs

• Prachya Boonkwan (Sirindhorn International Institute of Technology, Thailand)
• Shaojuan Wu (Shanxi University, China)

Local Organizer

• National Electronics and Computer Technology Center (NECTEC), Thailand
• Artificial Intelligence Association of Thailand (AIAT), Thailand

-----------------------------------------------------------------------