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 2025 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. At least five high quality papers will be invited for a special issue of the Knowledge and Information Systems Journal, in an expanded and revised form. In addition, at least eight quality papers will be invited for a special issue of Data Intelligence Journal in an expanded and revised form with at least 30% difference.

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 keep aspects of knowledge discover and graph learning.In addition to the above topics, authors can also select and target the following

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