Probabilistic Topic Maps?

Probabilistic Soft Logic

From the webpage:

Probabilistic soft logic (PSL) is a modeling language (with accompanying implementation) for learning and predicting in relational domains. Such tasks occur in many areas such as natural language processing, social-network analysis, computer vision, and machine learning in general.

PSL allows users to describe their problems in an intuitive, logic-like language and then apply their models to data.

Details:

  • PSL models are templates for hinge-loss Markov random fields (HL-MRFs), a powerful class of probabilistic graphical models.
  • HL-MRFs are extremely scalable models because they are log-concave densities over continuous variables that can be optimized using the alternating direction method of multipliers.
  • See the publications page for more technical information and applications.

This homepage lists three introductory videos and has a set of slides on PSL.

Under entity resolution, the slides illustrate rules that govern the “evidence” that two entities represent the same person. You will also find link prediction, mapping of different ontologies, discussion of mapreduce implementations and other materials in the slides.

Probabilistic rules could be included in a TMDM instance but I don’t know of any topic map software that supports probabilistic merging. Would be a nice authoring feature to have.

The source code is on GitHub if you want to take a closer look.

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