Keynote at ADCOM 2016
Title: Goal-directed Knowledge Graphs
Given a set of documents from a specific domain (e.g., medical research journals), how do we automatically build a Knowledge Graph (KG) for that domain? Automatic identification of relations and their schemas, i.e., type signature of arguments of relations (e.g., undergo(Patient, Surgery)), is an important first step towards this goal. We refer to this problem as Relation Schema Induction (RSI). In this talk, I shall present SICTF, a novel tensor factorization method for relation schema induction. SICTF factorizes Open Information Extraction (OpenIE) triples extracted from a domain corpus along with additional side information in a principled way to induce relation schemas. I shall also present experimental results and avenues for further work.
Partha Talukdar is an Assistant Professor in the Department of Computational and Data Sciences (CDS) and Department of Computer Science and Automation (CSA) 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, and Cognitive Neuroscience, with particular interest in large-scale learning and inference. Partha is a recipient of Google’s Focused Research Award and Accenture Open Innovation Award. He is a co-author of the book on Graph-based Semi-Supervised Learning published by Morgan Claypool Publishers.