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A Tutorial on Logic-Based Approaches to SRL
This video was recorded at ILP/MLG/SRL collocated International conferences/workshops on learning from relational, graph-based and probabilistic knowledge, Leuven 2009. The relations in Statistical Relational Learning are often expressed using first-order logic, leading to formalisms which combine both logical and probabilistic representations. In this talk I intend to explain the most important consequences of adopting a logical approach to SRL. Defining distributions over 'possible worlds' is a common theme to many such approaches. Two prominent logic-based formalisms - Markov logic networks and PRISM programs - will be used as exemplars. Although the talk is tutorial in nature, I hope to make it interesting to those already familiar with this area!
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
