IJCKG 2026: 15th International Joint Conference on Knowledge Graphs
November 19–21, 2026, Bangkok, Thailand
https://ijckg2026.aiat.or.th

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*** 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
Education Track
In-Use Track
Industry Forum
Workshops
Evaluation Challenges

*** Call for Research Track Papers ***

*** 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
*** Important Dates (Research Track) ***

Abstract Submission deadline: June 23,2026
Full Paper Submission Deadline: June 30, 2026
Acceptance Notification: August 26, 2026
All deadlines are 23:59 AoE (Anywhere on Earth)

*** Submission Guidelines ***

Authors are invited to submit original, unpublished research papers. Submissions must be written in English and formatted according to the Springer LNCS/LNAI guidelines.

Full papers should not exceed 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 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.

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