Abstract
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.
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© 2013 Springer-Verlag Berlin Heidelberg
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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. https://doi.org/10.1007/978-3-642-39617-5_1
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DOI: https://doi.org/10.1007/978-3-642-39617-5_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39616-8
Online ISBN: 978-3-642-39617-5
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