  Relational databases are valuable resources for learning novel and
interesting relations and concepts. In order to constraint the search through
the large space of candidate definitions, users must tune the algorithm by
specifying a language bias. Unfortunately, specifying the language bias is done
via trial and error and is guided by the expert's intuitions. We propose
AutoBias, a system that leverages information in the schema and content of the
database to automatically induce the language bias used by popular relational
learning systems. We show that AutoBias delivers the same accuracy as using
manually-written language bias by imposing only a slight overhead on the
running time of the learning algorithm.
