Probabilistic Similarity Logic

Conference Paper, Proceedings of the 2010 conference on Uncertainty in Artificial Intelligence
Presented at: Conference on Uncertainty in Artificial Intelligence, held on Santa Catalina Island, CA, USA from July 8th - July 11th, 2010
Authors: Matthias Broecheler, Lilyana Mihalkova, Lise Getoor
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Data set used in the experiments

Abstract
Many machine learning applications require the ability to learn from and reason about noisy multi-relational data. To address this, several effective representations have been developed that provide both a language for expressing the structural regularities of a domain, and principled sup- port for probabilistic inference. In addition to these two aspects, however, many applications also involve a third aspect–the need to reason about similarities–which has not been directly supported in existing frameworks. This paper introduces probabilistic similarity logic (PSL), a general-purpose framework for joint reason- ing about similarity in relational domains that incorporates probabilistic reasoning about similarities and relational structure in a principled way. PSL can integrate any existing domain- specific similarity measures and also supports reasoning about similarities between sets of entities. We provide efficient inference and learn- ing techniques for PSL and demonstrate its effectiveness both in common relational tasks and in settings that require reasoning about similarity.