MSR Montreal Pizza & AI Distinguished Lecture Series / Série de conférences émérites (MSR Montréal Pizzas et IA)

Deep Learning for Knowledge Representation and Reasoning

Andrew McCallum
Director of Center for Data Science, College of Information and Computer Science, University of Massachusetts Amherst

Tuesday, September 24, 2019 | Mardi, 24 septembre 2019
4:30 PM – 6:00 PM EST

Abstract

Knowledge gathering, representation, and reasoning are among the fundamental challenges of artificial intelligence. Large-scale repositories of knowledge about entities, relations, and their abstractions are known as “knowledge bases.” Most major technology companies now have substantial efforts in knowledge base construction. But how should knowledge in KBs be represented? Information retrieval and QA simply operate on raw text. Traditional KGs, like Cyc and Freebase, operate on human-engineered symbolic schemas. Massive latent learned representations, like those in BERT, have recently been explored for their knowledge-holding capacity. In this talk I will advocate for our ‘Universal Schema’ approach—a “middle way,” incorporating aspects of non-parametric raw-text, human ontologies, and large latent representations. After briefly reviewing foundational work on Universal Schema, I will introduce new research in (1) chains of reasoning, using reinforcement learning to guide the efficient search for meaningful chains, (2) aligning taxonomies and representing common sense with box-shaped embeddings, and (3) entity resolution by large-scale non-greedy clustering via Poincare embeddings.

Biography

Andrew McCallum is a Distinguished 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 300 papers in many areas of AI, including natural language processing, machine learning and reinforcement learning; his work has received over 60,000 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, ACM 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, Oracle, Amazon, and others. He was the General Chair for the International Conference on Machine Learning (ICML) 2012, and from 2014 to 2017 served the President of the International Machine Learning Society. He is a 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 team’s work on open peer review can be found at http://openreview.net. McCallum’s web page is http://www.cs.umass.edu/~mccallum.