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Enabling Machines to Understand Human Language by Knowledge Graphs
Date: 2016/12/5             Browse: 194
Seminar Topic: Enabling Machines to Understand Human Language by Knowledge Graphs

Speaker: Yanghua Xiao
Time: Dec. 5, 3:30 p.m. - 4:30 p.m.
Venue:  Room 1A-200, SIST Building

Abstract: 
One of the bottlenecks in machine intelligence is that machines have limited cognitive capability to understand data or text in the form of human language. One of the prerequisites to enpower machines with natural language understanding capability is the availability of the large scale knowledge bases of rich semantic information. Now, with more and more online knowledge bases (also known as knowledge graphs) being published, we have the opportunity to build smart machines that can understand human language. In this talk, I will review the recent progress to enable machines with the cognitive ability to understand natural language and discuss some open problems.

Biography:  
Yanghua Xiao now is an associate professor of computer science at Fudan University. His research interest includes big data management and mining, graph database, knowledge graph. Recently, he has published 70+ papers in international leading journals and top conferences, including SIGMOD,VLDB, ICDE, IJCAI, AAAI etc. He is the PI or Co-PI of 30+ projects supported by governments and Microsoft, IBM, China Telecom, Baidu, Huawei, Xiao I Robot etc. He regularly serves as the reviewer of 8+ funding agencies of government and PC member of 50+ international conference including IJCAI, SIGKDD, ICDE, WWW, CIKM, ICDM, COLING, DASFAA etc, associate Editor of Frontier of Computer Science, and reviewers of 20+ leading journals. He is the director of KW@FUDAN(Knowledge Works at Fudan, kw.fudan.edu.cn) and vice director of Shanghai Internet Big Data Engineering Center. He is also a chief scientist or senior adviser of big companies in China working on Web Information Management, Artificial Intelligence or Big Data Processing.

Seminar 16080