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Prof. LI QingChair Professor of Data Science and Head Director of FIEEE; FIET/IEE; DMCCF
Brief Introduction: Qing Li is currently a Chair Professor (Data Science) and the Head of the Department of Computing, the Hong Kong Polytechnic University. Formerly, he was the founding Director of the Multimedia software Engineering Research Centre (MERC), and a Professor at City University of Hong Kong where he worked in the Department of Computer Science from 1998 to 2018. Prior to these, he has also taught at the Hong Kong University of Science and Technology and the Australian National University (Canberra, Australia). Prof. Li served as a consultant to Microsoft Research Asia (Beijing, China), Motorola Global Computing and Telecommunications Division (Tianjin Regional Operations Center), and the Division of Information Technology, Commonwealth Scientific and Industrial Research Organization (CSIRO) in Australia. He has been an Adjunct Professor of the University of Science and Technology of China (USTC) and the Wuhan University, and a Guest Professor of the Hunan University (Changsha, China) where he got his BEng. degree from the Department of Computer Science in 1982. He is also a Guest Professor (Software Technology) of the Zhejiang University (Hangzhou, China) -- the leading university of the Zhejiang province where he was born. Title: Knowledge Graph Construction, Reasoning, and Manipulation: a Case Study in Education Domain Abstract: In recent years, knowledge graphs (KGs) have attracted tremendous interest and attention from both industry and academia, as evidenced by the many types of KGs developed including encyclopedia KGs, commonsense KGs, and KGs for medical science, covering a wide range of applications domains like search engines, question-answering and recommendations. For different application domains, however, the ways of constructing, reasoning, and manipulating KGs are quite different. In this talk, I shall introduce a collaborative project of building a university curriculum platform (called K-Cube) based on educational KGs. Among various functions and components, K-Cube supports a novel course KG construction framework guided by a standard ontology. To reduce the redundancy, we learn a backbone based on related Wiki data items and hierarchy, thereby avoiding to use named-entity recognition. As part of the reasoning, we design a machine reading comprehension task with pre-defined questions to extract relations, thereby improving the accuracy. Furthermore, KG Views are devised to support more advanced applications such as deriving instruction plans, for which two-way synchronization is supported to accommodate editing changes on the source KG and/or the derived views. In addition, KG manipulation operations including visualization (in both 2D and 3D spaces), navigation, and utilization have been developed and are to be introduced through an experimental prototype of KCube we have implemented. The ample facilities of K-Cube greatly accommodate learning path/material recommendations, effective academic advising with content exploration, and efficient course management, among other advantages.
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