中国计算机学会青年计算机科技论坛

CCF Young Computer Scientists & Engineers Forum

CCF YOCSEF

于2018年8月18日（星期六）10:00-12:00

在天津大学北洋园校区55教学楼B202会议室

学术报告会

人工智能新发展中的知识表示与应用

程 序

9:30-10:00 签到

10:00      报告会开始

10:00-11:00

特邀讲者：James A. Hendler 美国伦斯勒理工学院 教授

报告题目：Knowledge Representation in the Era of Deep Learning, Watson and the Semantic Web

11:00-11:30

特邀讲者： 张清鹏 博士 香港城市大学 助理教授

报告题目：Semantically Enhanced Medical Information Retrieval System: A Tensor Factorization Based Approach

11:30-12:00  自由讨论

12:00  会议结束 & 合影

执行主席：王鑫   博士 天津大学 副教授、YOCSEF天津副主席

执行主席：朱鹏飞 博士 天津大学 副教授、YOCSEF天津AC委员

参会者：IT领域专业人士、研究生、媒体、其他有兴趣者等

联系人：王鑫, wangx@tju.edu.cn

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学术报告会介绍

人工智能新发展中的知识表示与应用

特邀讲者：James A. Hendler

James Hendler is the Director of the Institute for Data Exploration and Applications and the Tetherless World Professor of Computer, Web and Cognitive Sciences at RPI. He also heads the RPI-IBM Center for Health Empowerment by Analytics, Learning and Semantics (HEALS) and serves as a Chair of the Board of the UK’s charitable Web Science Trust. Hendler has authored over 400 books, technical papers and articles in the areas of Semantic Web, artificial intelligence, agent-based computing and high performance processing. One of the originators of the “Semantic Web,” Hendler was the recipient of a 1995 Fulbright Foundation Fellowship, is a former member of the US Air Force Science Advisory Board, and is a Fellow of the AAAI, BCS, the IEEE, the AAAS and the ACM. He is also the former Chief Scientist of the Information Systems Office at the US Defense Advanced Research Projects Agency (DARPA) and was awarded a US Air Force Exceptional Civilian Service Medal in 2002. He is also the first computer scientist to serve on the Board of Reviewing editors for Science, co-editor-in-chief of the journal Data Intelligence, and an associate editor of Big Data. In 2010, Hendler was named one of the 20 most innovative professors in America by Playboy magazine and was selected as an “Internet Web Expert” by the US government. In 2012, he was one of the inaugural recipients of the Strata Conference “Big Data” awards for his work on large-scale open government data. In 2013, he was appointed as the Open Data Advisor to New York State and in 2015 appointed a member of the US Homeland Security Science and Technology Advisory Committee and in 2016, became a member of the National Academies Board on Research Data and Information. In 2017, Hendler joined the Director’s Advisory Committee for the National Security Directorate of the Pacific Northwest National Laboratory.

报告提要：A burst in optimism (and unwarranted fear) has grown around a number of technologies that are high impact and able to solve problems that have challenged AI researchers for years. The over-enthusiasm that often follows such breakthroughs has caused some to declare (yet again) that it is the end of “knowledge representation” as AI moves into a world dominated by neural networks, data mining and the knowledge graph. In this talk, I argue that these technologies, while extremely powerful separately, are not only still a long way from human intelligence, but cannot get there without a level of knowledge and reasoning beyond what is currently available to these techniques, On the other hand, I also argue that taking these technologies into new and harder realms will require rethinking what traditional knowledge representation is and how it is used. Some early examples of work aimed at joining the approaches will be presented.

特邀讲者：张清鹏

Qingpeng Zhang received the B.S. degree in Automation from Huazhong University of Science and Technology in 2009, and the Ph.D. degree in Systems and Industrial Engineering with a minor in Management Information Systems from The University of Arizona in 2012. Prior to joining CityU, he worked as a Postdoctoral Research Associate with the Department of Computer Science at Rensselaer Polytechnic Institute. He also worked as an intern scientist at the Pacific Northwest National Laboratory and the Chinese Academy of Sciences. He is an Associate Editor of IEEE Transactions on Computational Social Systems, IEEE Transactions on Intelligent Transportation Systems, and PLoS ONE. His research interests include social informatics and social computing, complex networks, healthcare data analytics, and semantic web.

报告提要：Integrating the medical knowledge bases has the potential to improve the information retrieval performance through incorporating medical domain knowledge for relevance assessment. However, this is not a trivial task due to the challenges to effectively utilize the domain knowledge in the medical knowledge bases. In this research, we proposed a novel medical information retrieval system with a two-stage tensor-factorization-based query expansion strategy, which is able to effectively model and incorporate the latent semantic associations to improve the performance. Experiments with the TREC CDS data set showed that the performance of the proposed system is significantly better than the baseline system and the systems reported in TREC CDS 2014 conference, and is comparable with the state-of-the-art systems. This research also demonstrated the capability of tensor-based semantic enrichment methods for medical information retrieval tasks.

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