Statistical Predicate Invention

by Stanley Kok and Pedro Domingos

This paper talks about how to find statistical predicate (SPI problems, statistical predicate invention). This, from another talk given by Domingos, is equivalent to find latent variables in traditional probabilistical learning, one of the ten most important problems in the following decades in machine learning. The setting of this paper is second-order Markov logic.

Their proposed approach for this problem is MRC (multiple relational clustering). The multiple relational clustering is interpreted via a simple example in the paper: one's technical skills and hobbies should be mined from different groups (clusters) of people, e.g. coworkers may share similar technical skills while friends share similar hobbies. The relational clustering is to find the latent relationship between people, and therefore ultimately finds who are coworkers, friends (latent predicates or r.v.). The resulting algorithm is not easy to understand. It looks like a clustering algorithm in logic language.

There is another piece of work (infinite relational model) using CRP in relational modelling, which I think should be very interesting. We will try to see the details later.