Probabilistic Soft Logic: A Scalable Approach for Markov Random Fields over Continuous-Valued Variables

Abstract

AbstractMany problems in AI require dealing with both relational structure and uncertainty. As a consequence, there is a growing need for tools that facilitate the development of complex probabilistic models with relational structure. These tools should combine high-level modeling languages with general purpose algorithms for inference in the resulting probabilistic models or probabilistic programs. A variety of such frameworks has been developed recently, based on ideas from graphical models, relational logic, or programming languages. In this talk, I will give an overview of our recent work on probabilistic soft logic (PSL), a framework for collective, probabilistic reasoning in relational domains. PSL models have been developed in a variety of domains, including collective classification, entity resolution, ontology alignment, opinion diffusion, trust in social networks, and modeling group dynamics.