Probabilistic Similarity Logic

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 support 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 reasoning about similarity in relational domains that incorporates probabilistic reasoning about similarities and relational structure in a principled way. PSL can integrate any existing domainspecific similarity measures and also supports reasoning about similarities between sets of entities. We provide efficient inference and learning techniques for PSL and demonstrate its effectiveness both in common relational tasks and in settings that require reasoning about similarity.

Extracted Key Phrases

5 Figures and Tables

0204020102011201220132014201520162017
Citations per Year

148 Citations

Semantic Scholar estimates that this publication has 148 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Broecheler2010ProbabilisticSL, title={Probabilistic Similarity Logic}, author={Matthias Broecheler and Lilyana Mihalkova and Lise Getoor}, booktitle={UAI}, year={2010} }