Bottom-up learning of logic programs for information extraction from hypertext documents

Abstract

We present an inductive logic programming bottom-up learning algorithm (BFOIL) for synthesizing logic programs for multi-slot information extraction from hypertext documents. BFOIL learns from positive examples only and uses a logical representation for hypertext documents based on the document object model (DOM). We briefly discuss several BFOIL refinements and show very promising results of our IE system LIPX in comparison to state of the art IE systems.