ICKG 2026 (IEEE International Conference on Knowledge Graph) is an academic conference held in Shenyang, China on 2026-11-12. The paper submission deadline is 2026-06-19. Acceptance notifications are sent on 2026-08-31.
The annual IEEE International Conference on Knowledge Graph (ICKG) provides a premier international forum for presentation of original research results in knowledge discovery and graph learning, discussion of opportunities and challenges, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of knowledge discovery from data, with a strong focus on graph learning and knowledge graph, including algorithms, software, platforms.
ICKG 2026 intends to draw researchers and application developers from a wide range of areas such as knowledge engineering, representation learning, big data analytics, statistics, machine learning, pattern recognition, data mining, knowledge visualization, high performance computing, and World Wide Web etc. By promoting novel, high quality research findings, and innovative solutions to address challenges in handling all aspects of learning from data with dependency relationship.
All accepted papers will be published in the conference proceedings by the IEEE Computer Society.
Awards, including Best Paper, Best Paper Runner up, Best Student Paper, Best Student Paper Runner up, will be conferred at the conference, with a check and a certificate for each award.
The conference also features a survey track to accept survey papers reviewing recent studies in all aspects of knowledge discovery and graph learning.
Topics of Interest
Topics of interest include, but are not limited to:
Foundations, algorithms, models, and theory of knowledge discovery and graph learning
Knowledge engineering with big data
Machine learning, data mining, and statistical methods for data science and engineering
Acquisition, representation and evolution of fragmented knowledge
Fragmented knowledge modeling and online learning
Knowledge graphs and knowledge maps
Graph learning security, privacy, fairness, and trust
Interpretation, rule, and relationship discovery in graph learning
Geospatial and temporal knowledge discovery and graph learning
Ontologies and reasoning
Topology and fusion on fragmented knowledge
Visualization, personalization, and recommendation of Knowledge Graph navigation and interaction
Knowledge Graph systems and platforms, and their efficiency, scalability, and privacy
Applications and services of knowledge discovery and graph learning in all domains including web, medicine, education, healthcare, and business
Big knowledge systems and applications
Crowdsourcing, deep learning and edge computing for graph mining
Large language models and applications
Open source platforms and systems supporting knowledge and graph learning
Datasets and benchmarks for graphs
Neurosymbolic & Hybrid AI systems
Graph Retrieval Augmented Generation
Survey Track: Survey paper reviewing recent study in key aspects of knowledge discovery and graph learning.
Special Track Topics
Each special track is handled by respective special track chairs, and the papers are also included in the conference proceedings.
Special Track 01: KGC and Knowledge Graph Building
Special Track 02: KR and KG Reasoning
Special Track 03: KG and Large Language Model
Special Track 04: GNN and Graph Learning
Special Track 05: QA and Graph Database
Special Track 06: KG and Multi-modal Learning
Special Track 07: KG and Knowledge Fusion
Special Track 08: Industry and Applications