Probabilistic Soft Logic: A Scalable Approach for Markov Random Fields over Continuous-Valued Variables
- Cite this paper as:
- Getoor L. (2013) Probabilistic Soft Logic: A Scalable Approach for Markov Random Fields over Continuous-Valued Variables. In: Morgenstern L., Stefaneas P., Lévy F., Wyner A., Paschke A. (eds) Theory, Practice, and Applications of Rules on the Web. RuleML 2013. Lecture Notes in Computer Science, vol 8035. Springer, Berlin, Heidelberg
Many 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.