Call for Papers
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
Submission Guidelines
Paper submissions should be no longer than 8 pages, in the IEEE 2-column format, including the bibliography and any possible appendices. Submissions longer than 8 pages will be rejected without review. All submissions will be reviewed by the Program Committee based on technical quality, originality, significance, and clarity.
For Survey Track Papers: Please preface the descriptive paper title with “Survey:”, followed by the actual paper title. For example, a paper entitled “A Literature Review of Streaming Knowledge Graph”, should be changed as “Survey: A Literature Review of Streaming Knowledge Graph”. This is for the reviewers and chairs to clearly bid and handle the papers. Once the paper is accepted, the word, such as “Survey:”, can be removed from the camera-ready copy.
For Special Track Papers: Please preface the descriptive paper title with “SS##:”, where “##” is the two digits special track ID. For example, a paper entitled “Incremental Knowledge Graph Learning”, intended to target Special Track 01 (Machine learning and knowledge graph) should be changed as “SS01: Incremental Knowledge Graph Learning”.
All manuscripts are submitted as full papers and are reviewed based on their scientific merit. The reviewing process is single blind, meaning that each submission should list all authors and affiliations. There is no separate abstract submission step. There are no separate industrial, application, or poster tracks. Manuscripts must be submitted electronically in the online submission system. No email submission is accepted.