Towards Automatically Setting Language Bias in Relational Learning

Abstract

Relational databases are valuable resources for learning novel and interesting relations and concepts. Relational learning algorithms learn the definition of new relations in terms of the existing relations in the database. In order to constraint the search through the large space of candidate definitions, users must specify a language bias. Unfortunately, specifying the language bias is done via trial and error and is guided by the expert's intuitions. Hence, it normally takes a great deal of time and effort to effectively use these algorithms. We report our on-going work on building AutoMode, a system that leverages information in the schema and content of the database to automatically induce the language bias used by popular relational learning algorithms.