You are here

Extracting Declarative and Procedural Knowledge from Documents and Videos on the Web

December 2, 2014 -
2:00pm to 3:00pm
32-G449 KIVA
Speaker Name: 
Kevin Murphy, Research Scientist, Google

We describe how we built a very large probabilistic database of declarative facts,  called "Knowledge Vault", by applying "machine reading" to the web. This approach extends previous work, such as NELL and YAGO, by leveraging existing knowledge bases as a form of "prior". We also discuss our new nascent efforts to extract procedural knowledge from videos on the web. This requires training visual detectors from weakly labeled data. We give an example where we attempt to interpret cooking videos by aligning the frames to the steps of a recipe.

Kevin Murphy is a research scientist at Google in Mountain View, California, where he works on AI, machine learning, computer vision and NLP. Before joining Google in 2011, he was an associate professor of computer science and statistics at the University of British Columbia in Vancouver, Canada. Before starting at UBC in 2004, he was a postdoc at MIT. Kevin got his BA from U. Cambridge, his MEng from U. Pennsylvania, and his PhD from UC Berkeley. He has published over 80 papers in refereed conferences and journals, as well as an 1100-page textbook called "Machine Learning: a Probabilistic Perspective" (MIT Press, 2012), which was awarded the 2013 DeGroot Prize for best book in the field of Statistical Science. Kevin is also the (co) Editor-in-Chief of JMLR (the Journal of Machine Learning Research).