Probabilistic Similarity Logic

Matthias Broecheler, Lilyana Mihalkova, Lise Getoor
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:73-82, 2010.

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 domain-specific 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-broecheler10a, title = {Probabilistic Similarity Logic}, author = {Broecheler, Matthias and Mihalkova, Lilyana and Getoor, Lise}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {73--82}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/broecheler10a/broecheler10a.pdf}, url = {https://proceedings.mlr.press/r8/broecheler10a.html}, 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 domain-specific 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Probabilistic Similarity Logic %A Matthias Broecheler %A Lilyana Mihalkova %A Lise Getoor %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-broecheler10a %I PMLR %P 73--82 %U https://proceedings.mlr.press/r8/broecheler10a.html %V R8 %X 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 domain-specific 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. %Z Reissued by PMLR on 04 October 2026.
APA
Broecheler, M., Mihalkova, L. & Getoor, L.. (2010). Probabilistic Similarity Logic. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:73-82 Available from https://proceedings.mlr.press/r8/broecheler10a.html. Reissued by PMLR on 04 October 2026.

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