报告人简历：
Kevin C. Chang is an Assistant Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign. He received a PhD in Electrical Engineering in 2001 from Stanford University. His research interests are in large scale information access, with emphasis on Web information integration and ranking-based data retrieval. He has received an NSF CAREER Award in 2002, an NCSA Faculty Fellow Award in 2003, and IBM Faculty Awards in 2004 and 2005.
URL: http://www-faculty.cs.uiuc.edu/~kcchang

内容简介：With so much structured data on the Web, we often search for various "stuff" (e.g., phone numbers, airfares, jobs) rather than Web pages-- but current search engines take us only to pages and only one page at a time. Can we deepen the search into reaching structured data directly, and across multiple sources holistically? I will discuss two major barriers and our dual efforts, leveraging DB/IR techniques in parallel: On the one hand, how to reach structured data on the deep Web? Search must build upon DB concepts to be "query aware" for retrieving data hidden *behind* Web pages. On the other hand, how to find structured data on the surface Web? Search must extend IR techniques to be "structure aware" for exploring data hidden *within* HTML pages. Towards these dual challenges, I will report the MetaQuerier/WISDM efforts and demo current prototypes.