Probabilistic Similarity Logic

Details Probabilistic Similarity Logic Probabilistic Similarity Logic

2012-03-15

arxiv.org/abs/1203.3469v1

Computer Science

Artificial Intelligence

Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Probabilistic Similarity Logic does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Probabilistic Similarity Logic, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Probabilistic Similarity Logic will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-32081