Campus Event Calendar
Event Entry
What and Who
Scalable Collective Inference using Probabilistic Soft Logic
Lise Getoor
University of California, Santa Cruz
MPI-INF Distinguished Lecture
Lise Getoor joined the Computer Science Department
at the University of California, Santa Cruz in November, 2013.
Prior to that, she was a professor in the Computer Science
Department at the University of Maryland, College Park (2001-2013).
Her primary research interests are in machine learning and
reasoning with uncertainty, applied to graphs and structured data.
She also works in data integration, social network analysis and
visual analytics. She has eight best paper awards, an NSF Career
Award, was PC co-chair for ICML 2011, and is an Association for
the Advancement of Artificial Intelligence (AAAI) Fellow. She
received her Ph.D. from Stanford University in 2001, her M.S.
from UC Berkeley, and her B.S. from UC Santa Barbara. For more
information, see http://www.cs.umd.edu/~getoor
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI
Public Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Monday, 24 March 2014
14:15
60 Minutes
E1 4
024
Saarbrücken
Abstract
One of the challenges in big data analytics lies in being able to
reason collectively about extremely large, incomplete, noisy
interlinked data. Some of the common collective inference patterns
include: collective classification (predicting missing labels in a
network), link prediction (predicting missing relationships),
community detection (discovering clusters of interlinked entities),
and entity resolution (determining when two references refer to the
same entity). In this talk I will overview our recent work on
probabilistic soft logic (PSL), a framework for collective,
probabilistic reasoning in relational domains. PSL is able to reason
holistically about both entity attributes and relationships among the
entities. The underlying mathematical framework, which we refer to as
a hinge-loss Markov random field, supports extremely efficient
inference. I will survey several applications of PSL to problems
in computational social science and knowledge graph identification.
Our recent results show that by building on state-of-the-art optimization
methods in a distributed implementation, we can solve large-scale problems
with millions of random variables orders of magnitude faster than existing
approaches.
Joint Work with Stephen Bach, Bert Huang, Matthias Broecheler,
Jay Pujara, Hui Miao, Angelika Kimmig, Ben London, Alex Memory, Stanley Kok, and Shobier Fahkraei.
reason collectively about extremely large, incomplete, noisy
interlinked data. Some of the common collective inference patterns
include: collective classification (predicting missing labels in a
network), link prediction (predicting missing relationships),
community detection (discovering clusters of interlinked entities),
and entity resolution (determining when two references refer to the
same entity). In this talk I will overview our recent work on
probabilistic soft logic (PSL), a framework for collective,
probabilistic reasoning in relational domains. PSL is able to reason
holistically about both entity attributes and relationships among the
entities. The underlying mathematical framework, which we refer to as
a hinge-loss Markov random field, supports extremely efficient
inference. I will survey several applications of PSL to problems
in computational social science and knowledge graph identification.
Our recent results show that by building on state-of-the-art optimization
methods in a distributed implementation, we can solve large-scale problems
with millions of random variables orders of magnitude faster than existing
approaches.
Joint Work with Stephen Bach, Bert Huang, Matthias Broecheler,
Jay Pujara, Hui Miao, Angelika Kimmig, Ben London, Alex Memory, Stanley Kok, and Shobier Fahkraei.
Contact
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Jennifer Müller, 03/12/2014 14:43 -- Created document.