CALL FOR PAPER ----------------------------------------------------------------------------- International Workshop on Knowledge Graph: Heterogenous Graph Deep Learning and Applications Co-located with ACM KDD 2021 (https://www.kdd.org/kdd2021/) https://suitclub.ischool.utexas.edu/KGKDD2021/index.html#home August 14, 2021, Singapore ------------------------------------------------------------------------------ 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). This workshop aims to bring researchers and practitioners to promote research and applications of KGs related to enhancing, scalability, interpretability of insights generated by deep learning. The topics of this workshop include (but not limit to): ● 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 Important Dates --------------- ● Workshop paper submissions: May 10 2021 ● Workshop paper notifications: June 10, 2021 ● Final submission of workshop program and materials: July 2, 2021 ● Workshop date: August 14-18, 2021 Submission Guidelines -------------------------- Authors can submit either full papers of 8 pages in length or short papers of 4 pages length in the ACM format (https://www.acm.org/publications/proceedings-template), with the "sigconf" option. Since we plan to follow single-blind review process, there is no need to anonymize the author list. Submissions can be made using EasyChair ( https://easychair.org/conferences/?conf=kgkdd2021). High quality submissions with substantial revisions will be invited to submit to Data Intelligence Journal published by MIT Press (https://www.mitpressjournals.org/loi/dint). Workshop Chairs ------------------------ Ying Ding, University of Texas at Austin, USA Bogdan Arsintescu, LinkedIN, USA Ching-Hua Chen, IBM, USA Haoyun Feng, Bloomberg, USA Francois Scharffe, Columbia University, USA Oshani Seneviratne, RPI, USA Juan Sequeda, data.world, USA Contact Information ----------------------- Ying Ding: ying.ding@austin.utexas.edu