Relational Learning via Propositional Algorithms: An Information Extraction Case Study

This paper develops a new paradigm for relational learning which allows for the representation and learning of relational information using propositional means. This paradigm suggests different tradeoffs than those in the traditional approach to this problem - the ILP approach - and as a result it enjoys several significant advantages over it. In particular, the new paradigm is more flexible and allows the use of any propositional algorithm, including probabilistic algorithms, within it. We evaluate the new approach on an important and relation-intensive task - Information Extraction - and show that it outperforms existing methods while being orders of magnitude more efficient.

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