Probabilistic Similarity Logic

TL;DR

Probabilistic Similarity Logic (PSL) is a new framework that enables joint probabilistic reasoning about similarities and relational structures, integrating domain-specific similarity measures for complex relational tasks.

Contribution

This paper introduces PSL, a novel framework that combines probabilistic reasoning with similarity measures in relational domains, supporting set-based similarity reasoning and efficient inference.

Findings

Effective in relational tasks involving similarity reasoning

Supports integration of domain-specific similarity measures

Demonstrates efficiency in inference and learning

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.