Prof. A. McCallum: Representation and Reasoning with Universal Schema Embeddings

Dear all,

Please help me welcome to our meetup another top AI scientist, Andrew McCallum, professor at the Computer Science department of the University of Massachusetts Amherst. Prof. McCallum has authored more than 250 papers with more than 45000 citations. In this talk he will introduce his latest research, a new framework for knowledge representation and reasoning that mixes different sources of inputs, using both logical and neural approaches based on the "universal schema" approach (full abstract below). Not to be missed!

Light refreshments will be served.

Note that this time we will meet on Wednesday (and not Thursday as usual in the past).

Abstract

Work in knowledge representation has long struggled to design schemas of entity- and relation-types that capture the desired balance of specificity and generality while also supporting reasoning and information integration from various sources of input evidence. In our "universal schema" approach to knowledge representation we operate on the union of all input schemas (from structured KBs to OpenIE textual patterns) while also supporting integration and generalization by learning vector embeddings whose neighbhorhoods capture semantic implicature. In this talk I will introduce universal schema, then describe recent work leading toward (a) having the textual entity- and relation-mentions themselves represent the KB, (b) using universal schema and neural attention models to provide generalization, (c) logical reasoning on top of this text-KB through multi-hop relational paths modeled by recurrent neural tensor networks, and (d) future work on reinforcement learning to guide the search for proofs of the answers to queries.

Bio: Andrew McCallum is a Professor and Director of the Information Extraction and Synthesis Laboratory, as well as Director of Center for Data Science in the College of Information and Computer Science at University of Massachusetts Amherst. He has published over 250 papers in many areas of AI, including natural language processing, machine learning and reinforcement learning; his work has received over 45,000 citations. He obtained his PhD from University of Rochester in 1995 with Dana Ballard and a postdoctoral fellowship from CMU with Tom Mitchell and Sebastian Thrun. In the early 2000's he was Vice President of Research and Development at at WhizBang Labs, a 170-person start-up company that used machine learning for information extraction from the Web. He is a AAAI Fellow, the recipient of the UMass Chancellor's Award for Research and Creative Activity, the UMass NSM Distinguished Research Award, the UMass Lilly Teaching Fellowship, and research awards from Google, IBM, Microsoft, and Yahoo. He was the General Chair for the International Conference on Machine Learning (ICML) 2012, and is the current President of the International Machine Learning Society, as well as member of the editorial board of the Journal of Machine Learning Research. For the past ten years, McCallum has been active in research on statistical machine learning applied to text, especially information extraction, entity resolution, social network analysis, structured prediction, semi-supervised learning, and deep neural networks for knowledge representation. His work on open peer review can be found at http://openreview.net (http://openreview.net/). McCallum's web page is http://www.cs.umass.edu/~mccallum