|Title||:||Graph-based methods for Web-scale Knowledge Harvesting|
|Speaker||:||Partha Talukdar (SERC, IISc Bangalore)|
|Details||:||Fri, 21 Nov, 2014 11:00 AM @ BSB 361|
|Abstract:||:||Abstract: Harvesting knowledge from web-scale text datasets has emerged as an interesting and promising area of research over the last few years, resulting in the construction of several large knowledge bases (KBs). Graphs provide a natural way to organize such harvested knowledge. Moreover, graph-based learning and inference methods have proved to be effective in the construction of such KBs. In this talk, I shall present an overview of my research in this area, with examples and applications of techniques ranging from graph-based semi-supervised learning to random walk inference over knowledge graphs.
About the Speaker : Partha Pratim Talukdar is an Assistant Professor in the Supercomputer Education and Research Centre (SERC) at the Indian Institute of Science (IISc), Bangalore. Before that, he was a Postdoctoral Fellow in the Machine Learning Department at Carnegie Mellon University, working with Tom Mitchell on the NELL project. Partha received his PhD (2010) in CIS from the University of Pennsylvania, working under the supervision of Fernando Pereira, Zack Ives, and Mark Liberman. Partha is broadly interested in Machine Learning, Natural Language Processing, Data Integration, and Cognitive Neuroscience, with particular interest in large-scale learning and inference over graphs. His past industrial research affiliations include HP Labs, Google Research, and Microsoft Research. He is a co-author of the book on Graph-based Semi-Supervised Learning published by Morgan Claypool Publishers.
This talk is supported partly by Yahoo! labs.