Using Statistics and Semantics to Solve Big (Graph) Data Problems

author: Lise Getoor, Department of Computer Science, University of California Santa Cruz
published: Aug. 22, 2017,   recorded: January 2015,   views: 0
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Description

Big data problems benefit from modeling both structure and uncertainty, so there is a growing need for tools to develop large, complex probabilistic models. These tools should combine high-level knowledge representation with general purpose, scalable algorithms for learning and inference. In this talk, I will survey some of the recent work from the statistical relational learning community on learning and inference in richly-structured, multi-relational network data. I will highlight both important developments and opportunities in which ideas from AI can have great impact on upcoming challenges within the machine learning, data science and data mining communities.

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Reviews and comments:

Comment1 Azizur Rahman, October 31, 2017 at 10:41 p.m.:

Excellent description has given in this video.

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