With huge amount of data mounting everywhere, it is essential to turn big data to big knowledge. Massive amounts of data are natural language text-based, unstructured, noisy, and untrustworthy, but are interconnected, potentially forming gigantic, interconnected information networks. If such text-rich data can be processed and organized into multiple typed, semi-structured heterogeneous information networks, organized knowledge can be mined from such networks. Most real-world applications that handle big data, including interconnected social networks, medical information systems, online e-commerce systems, or Web-based forum and data systems, can be structured into typed, heterogeneous social and information networks. For example, in a medical care network, objects of multiple types, such as patients, doctors, diseases, medication, and links such as visits, diagnosis, and treatments are intertwined together, providing rich information and forming heterogeneous information networks. Effective analysis of large-scale, text-rich heterogeneous information networks poses an interesting but critical challenge.
In this talk, we present an overview of our recent studies on construction and mining of text-rich heterogeneous information networks. We show that relatively structured heterogeneous information networks can be constructed from unstructured, interconnected, text data, and such relatively structured, heterogeneous networks brings tremendous benefits for data mining. Departing from many existing network models that view data as homogeneous graphs or networks, the text-based, semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and can uncover surprisingly rich knowledge from interconnected data. This heterogeneous network modeling will lead to the discovery of a set of new principles and methodologies for mining text-rich, interconnected data. We will also point out some promising research directions and provide arguments on that construction and mining of text-rich heterogeneous information networks could be a key to transforming big data to big knowledge.
Jiawei Han, Abel Bliss Professor 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 700 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, a BD2K (Big Data to Knowledge) center supported by NIH. He is a Fellow of ACM and Fellow of IEEE. He received 2004 ACM SIGKDD Innovations Award, 2005 IEEE Computer Society Technical Achievement Award, and 2009 IEEE Computer Society Wallace McDowell Award. His book “Data Mining: Concepts and Techniques” has been used popularly as a textbook worldwide.