Probabilistic Similarity Logic
  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.
