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Yizhou Sun "Bringing Additional Symbolic Knowledge for Knowledge Graph Reasoning"

Event Type
Seminar/Symposium
Sponsor
The Department of Computer Science, University of Illinois, BLENDER Lab
Location
https://illinois.zoom.us/j/8167899060?pwd=YkZrQ09zODRzL0txRGF5bnhWdmk0UT09
Virtual
wifi event
Date
Mar 12, 2021   1:00 pm  
Speaker
Dr. Yizhou Sun, Associate Professor, UCLA
Contact
Candice Steidinger
E-Mail
steidin2@illinois.edu
Phone
217-300-8564
Views
42

Abstract:

Knowledge graph has 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 completion. However, most of the existing knowledge graph embedding approaches treat knowledge graph as a complete, error-free, and flat data structure to store knowledge. In this talk, I will introduce two recent techniques developed in our lab to bring additional knowledge for better knowledge graph embedding. First, external knowledge represented as first-order logic is brought into knowledge graph embedding, which is able to address the uncertainty in knowledge graph and handle missing facts. Second, a unified embedding framework that incorporates ontological view KG into widely studied instance view KG will be introduced, which can seamlessly bring instance world and concept world together. Both techniques can significantly enhance the quality of KG embedding, on different downstream tasks, which also show a promising future direction in better knowledge graph reasoning.

 

Bio:

Yizhou Sun is an associate professor at department of computer science of UCLA. 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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