"Building Large Knowledge Bases by Mass Collaboration"

3/12/2004: 10:30am - 12:00pm
11th FL Large Conference Room

Abstract: Acquiring knowledge has long been the major bottleneck preventing the rapid spread of AI systems. Manual approaches are slow and costly. Machine-learning approaches have limitations in the depth and breadth of knowledge they can acquire. The spread of the Internet has made possible a third solution: building knowledge bases by mass collaboration, with thousands of volunteers contributing simultaneously. While this approach promises large improvements in the speed and cost of knowledge base development, it can only succeed if the problem of ensuring the quality, relevance and consistency of the knowledge is addressed, if contributors are properly motivated, and if the underlying algorithms scale. In this talk I will describe an architecture that meets all these desiderata. It uses first-order probabilistic reasoning techniques to combine potentially inconsistent knowledge sources of varying quality, and it uses machine-learning techniques to estimate the quality of knowledge. We have evaluated the approach using a series of synthetic knowledge bases and a pilot study in the domain of printer troubleshooting. (Joint work with Matt Richardson.)

About Pedro M. Domingos: Pedro Domingos is an assistant professor in the Department of Computer Science and Engineering at the University of Washington. His research interests are in artificial intelligence, machine learning and data mining. He received a PhD in Information and Computer Science from the University of California at Irvine, and is the author or co-author of over 100 technical publications. He is associate editor of JAIR, a member of the editorial board of the Machine Learning journal, and a co-founder of the International Machine Learning Society. He was program co-chair of KDD-2003, and has served on numerous program committees. He has received several awards, including a Sloan Fellowship, an NSF CAREER Award, a Fulbright Scholarship, an IBM Faculty Award, and best paper awards at KDD-98 and KDD-99.

Last updated: Mon Jun 19 17:44:06 2006