Call for Papers
IJCKG 2026 invites researchers and practitioners to present innovative research results and novel applications of Knowledge Graphs for agentic, multimodal, and retrieval-augmented intelligence.
An academic forum for the global Knowledge Graph community.
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 (NECTEC), Thailand and the Artificial Intelligence Association of Thailand (AIAT).
This theme 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.
It 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.
Submit to any of our tracks.
Topics of interest include, but are not limited to:
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
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
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
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
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
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
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
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
Authors are invited to submit original, unpublished research papers.
Formatting instructions and templates are available at the Springer LNCS proceedings guidelines.
Published by Springer in the LNAI series.
Accepted papers 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.