AutoMode: Relational Learning With Less Black Magic
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by
Jose Picado, Sudhanshu Pathak, Arash Termehchy, Alan Fern
2017
Abstract
Relational databases are valuable resources for learning novel and
interesting relations and concepts. Relational learning algorithms learn the
Datalog 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 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. Hence, it normally takes a great deal
of time and effort to effectively use these algorithms. In particular, it is
hard to find a user that knows computer science concepts, such as database
schema, and has a reasonable intuition about the target relation in special
domains, such as biology. We propose 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 systems. We show that
AutoMode delivers the same accuracy as using manually-written language bias by
imposing only a slight overhead on the running time of the learning algorithm.
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