Information Extraction from the Web: An Ontology-Based Method Using Inductive Logic Programming

Abstract

Relevant information extraction from text and web pages in particular is an intensive and time-consuming task that needs important semantic resources. Thus, to be efficient, automatic information extraction systems have to exploit semantic resources (or ontologies) and employ machine-learning techniques to make them more adaptive. This paper presents an Ontology-based Information Extraction method using Inductive Logic Programming that allows inducing symbolic predicates expressed in Horn clausal logic that subsume information extraction rules. Such rules allow the system to extract class and relation instances from English corpora for ontology population purposes. Several experiments were conducted and preliminary experimental results are promising, showing that the proposed approach improves previous work over extracting instances of classes and relations, either separately or altogether.