@article {14466,
	title = {Probabilistic similarity logic},
	journal = {Proceedings of the Conference on Uncertainty in Artificial Intelligence},
	year = {2010},
	month = {2010///},
	abstract = {Many machine learning applications require theability to learn from and reason about noisy
multi-relational data. To address this, several ef-
fective representations have been developed that
provide both a language for expressing the struc-
tural 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{\textendash}the need to reason
about similarities{\textendash}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 sim-
ilarities 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 en-
tities. We provide efficient inference and learn-
ing techniques for PSL and demonstrate its ef-
fectiveness both in common relational tasks and
in settings that require reasoning about similarity.
},
	author = {Br{\"o}cheler,M. and Mihalkova,L. and Getoor, Lise}
}
