Probabilistic Similarity Logic
It addresses the need for similarity reasoning in machine learning applications involving relational data, offering a novel framework.
The paper tackles the problem of learning from noisy multi-relational data by introducing probabilistic similarity logic (PSL), a framework that integrates reasoning about similarities and relational structure, and demonstrates its effectiveness in relational tasks and similarity-based settings.
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.