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Relational databases are valuable resources for learning novel andinteresting relations and concepts. In order to constraint the search throughthe large space of candidate definitions, users must tune the algorithm byspecifying a language bias. Unfortunately, specifying the language bias is donevia trial and error and is guided by the expert's intuitions. We proposeAutoBias, a system that leverages information in the schema and content of thedatabase to automatically induce the language bias used by popular relationallearning systems. We show that AutoBias delivers the same accuracy as usingmanually-written language bias by imposing only a slight overhead on therunning time of the learning algorithm.

Usable & Scalable Learning Over Relational Data With Automatic Language Bias