Faculty Candidate Seminar

Markov Logic: Representation, Inference and Learning

Daniel LowdPhD CandidateUniversity of Washington

Many applications of AI, including natural language processing, information extraction, bioinformatics, robot mapping, and social network analysis, have both relational and statistical aspects. Historically, there has been a divide between relational approaches based on first-order logic and statistical approaches based on probabilistic graphical models. Markov logic unifies the two by attaching weights to formulas in first-order logic, which are used as templates for constructing a Markov network.

In this talk, I will describe recent advances in Markov logic representation, algorithms, and applications. In particular, I will present my work on recursive Markov logic, which gives probabilistic models the full recursive capabilities of first-order logic. I will also show how faster and more efficient weight learning algorithms can be obtained by adapting ideas from convex optimization. Finally, I will discuss current work on combining learning with inference to make exact inference tractable even in very complex models such as Markov logic networks. I will illustrate these developments with applications to probabilistic databases, entity resolution, Web mining, and others.

Sponsored by

EECS - CSE Division