Knowledge Graph deals with fragmented knowledge from heterogeneous, autonomous information sources for complex and evolving relationships, in addition to domain expertise. The IEEE International Conference on Knowledge Graph (ICKG) provides a premier international forum for presentation of original research results in Knowledge Graph opportunities and challenges, as well as exchange and dissemination of innovative, practical development experience. The conference covers all aspects of Knowledge Graph, including algorithms, software, platforms, and applications for knowledge graph construction, maintenance, and inference. ICKG 2023 draws researchers and application developers from a wide range of Knowledge Graph related areas such as knowledge engineering, big knowledge, big data analytics, statistics, machine learning, pattern recognition, data mining, knowledge visualization, high performance computing, and World Wide Web. By promoting novel high quality research findings and innovative solutions to challenging Knowledge Graph problems, the conference seeks to continuously advance the state-of-the-art in Knowledge Graph.

Accepted papers will be published in the conference proceedings by the IEEE Computer Society. Awards will be conferred at the conference on the authors of the best paper and the best student paper.  High quality papers will be invited for a special issue of Knowledge and Information Systems Journal  in an expanded and revised form.

Topics of Interest

Topics of interest include, but are not limited to:

·  ​Foundations, algorithms, models, and theory of Knowledge Graph processing.
·  Knowledge engineering with big data.
·  Machine learning, data mining, and statistical methods for Knowledge Graph science and engineering.
·  Acquisition, representation, and evolution of fragmented knowledge.
·  Fragmented knowledge modeling and online learning.
·  Knowledge graphs and knowledge maps.
·  Knowledge graph security, privacy, and trust.
·  Knowledge graphs and IoT data streams.
·  Geospatial knowledge graphs.
·  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 Graph in all domains including web, medicine, education, healthcare, and business.
·  Big knowledge systems and applications.
·  Crowdsourcing, deep learning and edge computing for graph mining.
·  Rule and relationship discovery in knowledge graph computing.