模式识别学术大讲堂
Advanced Lecture Series in Pattern Recognition
题    目 (TITLE)：From Data to Knowledge: Construction and Exploration of Heterogeneous Information Networks
讲 座 人 (SPEAKER)：Abel Bliss Prof. Jiawei Han (University of Illinois at Urbana-Champaign)
主 持 人 (CHAIR)：Prof. Chenglin Liu
时    间 (TIME)：July 24(Friday), 2015, 10:30AM
地    点 (VENUE)：No.1 Conference Room (3rd floor), Intelligence Building
报告摘要（ABSTRACT）：
The real-world data are largely unstructured but interconnected.   The majority of such data is in the form of natural language text.  One of the grand challenges is to turn such massive data into actionable knowledge.  In this talk, we present our vision on how to turn massive unstructured, text-rich, but interconnected data into knowledge.  We propose a D2N2K (i.e., data-to-network-to-knowledge) paradigm, that is, first turn data into relatively structured heterogeneous information networks, and then mine such text-rich and structure-rich heterogeneous networks to generate useful knowledge.  We show why such a paradigm represents a promising direction and present some recent progress on the development of effective methods for construction and mining of structured heterogeneous information networks from text data.

报告人简介（BIOGRAPHY）：
Jiawei Han is Abel Bliss Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign.  He has been researching into data mining, information network analysis, database systems, and data warehousing, with over 600 journal and conference publications. He has chaired or served on many program committees of international conferences, including PC co-chair for KDD, SDM, and ICDM conferences, and Americas Coordinator for VLDB conferences.  He also served as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data and is serving as the Director of Information Network Academic Research Center supported by U.S. Army Research Lab, and Director of KnowEnG, an NIH funded Center of Excellence in Big Data Computing.  He is a Fellow of ACM and Fellow of IEEE, and received 2004 ACM SIGKDD Innovations Award, 2005 IEEE Computer Society Technical Achievement Award, 2009 IEEE Computer Society Wallace McDowell Award, and 2011 Daniel C. Drucker Eminent Faculty Award at UIUC.  His co-authored book "Data Mining: Concepts and Techniques" has been adopted as a textbook popularly worldwide.