Recursive Feature Generation for Knowledge-based Learning
This work addresses the challenge of leveraging structured knowledge for machine learning, offering a domain-specific improvement for text classification.
The paper tackles the problem of enhancing inductive learning algorithms by incorporating external knowledge bases, and demonstrates that their recursive feature generation algorithm significantly improves performance in text classification tasks.
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.