Recursive Feature Generation for Knowledge-based Learning
  When humans perform inductive learning, they often enhance the process with
background knowledge. With the increasing availability of well-formed
collaborative knowledge bases, the performance of learning algorithms could be
significantly enhanced if a way were found to exploit these knowledge bases. In
this work, we present a novel algorithm for injecting external knowledge into
induction algorithms using feature generation. Given a feature, the algorithm
defines a new learning task over its set of values, and uses the knowledge base
to solve the constructed learning task. The resulting classifier is then used
as a new feature for the original problem. We have applied our algorithm to the
domain of text classification using large semantic knowledge bases. We have
shown that the generated features significantly improve the performance of
existing learning algorithms.
