It is becoming increasingly clear that the next generation of web search and advertising will rely on a deeper understanding of user intent and task modeling, and a correspondingly richer interpretation of content on the web. How we get there, in particular, how we understand web content in richer terms than bags of words and links, is a wide open and fascinating question. I will discuss some of the options here, and look closely at the role that information extraction can play.

Speaker Bio

Raghu Ramakrishnan is Chief Scientist for Audience and Cloud Computing at Yahoo!, and is a Research Fellow, heading the Community Systems area in Yahoo! Research. He was Professor of Computer Sciences at the University of Wisconsin-Madison, and was founder and CTO of QUIQ, a company that pioneered question-answering communities, powering Ask Jeeves' AnswerPoint as well as customer-support for companies such as Compaq. His research has influenced query optimization in commercial database systems, and the design of window functions in SQL:1999. His paper on the Birch clustering algorithm received the SIGMOD 10-Year Test-of-Time award, and he has written the widely-used text "Database Management Systems" (with Johannes Gehrke).

He is Chair of ACM SIGMOD, on the Board of Directors of ACM SIGKDD and the Board of Trustees of the VLDB Endowment, and has served as editor-in-chief of the Journal of Data Mining and Knowledge Discovery, associate editor of ACM Transactions on Database Systems, and the Database area editor of the Journal of Logic Programming. Ramakrishnan is a Fellow of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE), and has received several awards, including a Distinguished Alumnus Award from IIT Madras, a Packard Foundation Fellowship in Science and Engineering, an NSF Presidential Young Investigator Award, and an ACM SIGMOD Contributions Award.