Discovering Latent Classes in Relational Data
We present a framework for learning abstract relational knowledge, with the aim of explaining how people acquire intuitive theories of physical, biological, or social systems. Our algorithm infers a generative relational model with latent classes, simultaneously determining the kinds of entities that exist in a domain, the number of these latent classes, and the relations between classes that are possible or likely. This model goes beyond previous category-learning models in psychology, which consider the attributes associated with individual categories but not the relationships that can exist between categories. We apply this domain-general framework in two specific domains: learning the structure of kinship systems and learning causal theories.