| 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–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 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.
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