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SEIT Lab, Department of Computer Science, University of Cyprus

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.

Tél.: [00357722892693];     Email.: george@ucy.ac.cy

• 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