View source for OntologySummit2017 Communique/Probabilistic Semantics

== Probabilistic Semantics ==
=== Opportunities ===
* Statistical Relational Learning (SRL)  Broad AI  needs to deal with both relational structure and uncertainty and a particular line of work focusing on the combination of probabilistic models with description logic is known as Probabilistic Semantic, and has progressively gained importance.
* Probabilistic Soft Logic (PSL) is a machine learning framework for developing probabilistic models. PSL uses first order logic rules as a template language for graphical models over random variables with soft truth values from the interval [0; 1].  The  underlying mathematical framework supports extremely efficient inference continuous optimization task, which can be solved efficiently.  PSL includes the ability to reason holistically about both entity attributes and relationships among the entities, along with ontological constraints.  In practice PSL has produced state-of-the-art results in many areas spanning natural language processing, social-network analysis, and computer vision.
*  With PSL large-scale knowledge graph extraction problems with millions of random variables orders of magnitude faster than existing approaches.
=== Challenges ===
(Ram will draft)
=== Future Prospects ===
=== References ===