Knowledge graph is essentially the knowledge base of semantic web, which is composed of entities (nodes) and relations (edges). As the representation of semantics, knowledge graphs can readily-easily formulate real-world entities, concepts, attributes, as well as their relations. All the specific features of knowledge graphs make it born with strong expressive ability and flexible modelling ability. At the same time, as a special kind of graph data, knowledge graphs are both human-readable and machine-friendly. With effective knowledge representation approaches, a variety of tasks can be resolved, including knowledge extraction, knowledge integration, knowledge management, and knowledge applications. Furthermore, efficient knowledge representation learning and reasoning can be one of the paths towards the emulation of high-level cognition and human-level intelligence. These trends naturally facilitate relevant downstream applications which inject structural knowledge into wide-applied neural architectures such as attention-based transformers and graph neural networks.
Recent years have witnessed the rapid growth in the number of academics and practitioners interested in knowledge graph and closely related areas. In particular, various deep neural network models have been developed for knowledge graph-based areas. Meanwhile, as knowledge graphs have been applied in various domains such as information retrieval, natural language understanding, question answering systems, recommender systems, financial risk control, etc., new challenges have emerged in the context of knowledge graphs from many perspectives including scalability, security, explainability, robustness, etc.
The workshop will bring together researchers and practitioners to discuss the fundamentals, methodologies, techniques, and applications of knowledge graphs. In this workshop, our goal is to contribute to the next generation of knowledge graphs and exploring them using artificial intelligence, data science, machine learning, network science, and other appropriate technologies.
Topics of interest include but not limited to:
This workshop would like to share exciting techniques to solve critical problems such as:
Authors are invited to submit original papers that must not have been submitted to or published in any other workshop, conference, or journal. The workshop will accept full papers describing completed work, work-in-progress papers with preliminary results, as well as position papers reporting inspiring and intriguing new ideas.
All papers should be no more than 10 pages in length (max 8 pages plus 2 extra pages), in the IEEE 2-column format ( https://www.ieee.org/conferences/publishing/templates.html ), including the bibliography and any possible appendices. Submissions longer than 10 pages will be rejected without review. Authors must complete a reproducibility checklist at the time of paper submission (the questions in PDF format) [ https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist-v2.0.pdf ].
All submissions will be peer-reviewed by members of the Program Committee and be evaluated for originality, quality and appropriateness to the workshop. Furthermore, as in previous years, papers that are not accepted by the main conference will be automatically sent to a workshop selected by the authors when the papers were submitted to the main conference. By the unique ICDM tradition, all accepted workshop papers will be published in the dedicated ICDMW proceedings published by the IEEE Computer Society Press.
To be added