Abstract: Knowledge graphs have received tremendous attention recently, due to its wide applications, such as search engines and Q&A systems. Knowledge graph embedding, which aims at representing entities as low-dimensional vectors, and relations as operators on these vectors, has been widely studied and successfully applied to many tasks, such as knowledge reasoning. In this tutorial, we will cover recent representation learning techniques for knowledge graphs, which contains three parts. First, we will review the knowledge graph representation techniques that are usually based on shallow embedding, such as TransE, DisMult, and RotatE. Second, we will discuss the recent progress on how to integrate additional symbolic information, such as logic rules and ontology, for better representation learning on knowledge graphs. In the third part, we will introduce graph neural networks (GNNs) and recent advances on GNNs for heterogeneous information networks, which can be considered as a general form of knowledge graph.

Bio: Yizhou Sun is an associate professor at department of computer science of UCLA. Prior to that, she was an assistant professor in the College of Computer and Information Science of Northeastern University. She received her Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2012. Her principal research interest is on mining graphs/networks, and more generally in data mining, machine learning, and network science, with a focus on modeling novel problems and proposing scalable algorithms for large-scale, real-world applications. She is a pioneer researcher in mining heterogeneous information network, with a recent focus on deep learning on graphs/networks. Yizhou has over 100 publications in books, journals, and major conferences. Tutorials of her research have been given in many premier conferences. She received 2012 ACM SIGKDD Best Student Paper Award, 2013 ACM SIGKDD Doctoral Dissertation Award, 2013 Yahoo ACE (Academic Career Enhancement) Award, 2015 NSF CAREER Award, 2016 CS@ILLINOIS Distinguished Educator Award, 2018 Amazon Research Award, and 2019 Okawa Foundation Research Grant.

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