Probabilistic Soft Logic

In: Conference| Publication

3 Dec 2010

There is a growing interest in methods for exploiting causal or correlational dependencies in structured domains. Exploiting such dependencies often results in improved predictive performance on complex inference tasks in diverse domains such as information integration, natural language processing, and computer vision. In this presentation, we introduce probabilistic soft logic (PSL), a general-purpose framework for expressing, reasoning about and learning structural dependencies.
PSL provides a declarative language tailored to relational domains that require reasoning about similarity and/or probability. Some of the novel aspects of PSL include a representation based on continuous valued random variables, efficient polynomial-time inference algorithms, native support for reasoning about sets, and the ability to estimate confidences values for predictions. This presentation provides a detailed account of PSL covering its mathematical foundation, logic programming semantics, inference and learning algorithms, scalability through parallelization, and different applications. We close with a demonstration of the PSL system implementation.


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2 Responses to Probabilistic Soft Logic



March 6th, 2015 at 2:57 am

hi,psl algorithm is my project in our university and i can’t find any reference in persian about this subject.please help me to understand about this algorithm.sorry for this scandal writing english from me.please help me.



October 6th, 2015 at 5:59 pm

Hello everyone, it’s my first pay a visit at this web page, and piece of writing is in fact fruitful
for me, keep up posting such articles or reviews.

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