Knowledge graph has left its footprint almost everywhere, from virtual assistant at our home, online shopping, self-driving car, to stock prediction. Our daily activities have closely intermingled with various applications powered by knowledge graph. It even enters to our healthcare to facilitate clinical decision making and improve hospital efficiency.Gartner has predicted that knowledge graph (i.e., connected data with semantically enriched context) applications and graph mining will grow 100% annually through 2022 to enable more complex and adaptive data science. Applying and developing novel deep learning methods on graphs is now one of the most heated topics with the highest demands from academia and industry. Graph convolutional neural networks, graph transformer, graph embedding and more have achieved great performance on various downstream tasks.
Knowledge graphs (KGs) are important resources for Artificial Intelligence (AI) solutions that seek to go beyond generating an insight, to interpreting the past, current and possible future contexts to which the insight applies. Not only can it be misleading to mine data without considering or providing context, disconnected insights can be of limited use in complex, real-world situations. To move beyond retail consumer applications and otherwise narrow-AI tasks, it is necessary to address several challenges. For example, few embedding methods can adequately deal with heterogeneous KGs which comprise different types of nodes and edges. However, this heterogeneity, if properly represented, has the potential to aid in the development of novel deep learning methods (e.g., by offering new ways for data augmentation, contrastive learning, and pre-training models).
The workshop will be open for the whole conference. Each submitted paper will be evaluated by three reviewers from the aspects of novelty, significance, technique sound, experiments, and presentations. The reviewers will be program committee members or researchers recommended by the members.
All papers submitted should have a maximum length of 8 pages and demo papers should be no more than 4 pages. All must be prepared using the ACM camera-ready template. Authors are required to submit their papers electronically in PDF format.
Building KGs using NLP
Heterogeneous graph embedding, graph transformer, and graph convolutional neural network
Contrastive learning in graph mining
Graph deep learning for semantic reasoning
Visual searching and browsing of KGs
Industrial applications of KGs: banking, financing, retail, healthcare, medicine, pharma, etc
KGs in computer vision, medical imaging
KGs for explainable AI
KGs for AI ethics and misinformation