Information Extraction for learning of Ontology Instances

Ontology is a crucial building block of semantic web, which is accepted as the most advanced knowledge representation model. But ontology learning is a big obstacle for its complexity and labor-denseness. We use rule-based information extraction (IE) to get instances from text. There is great challenge for the adaptivity of IE for ontology learning, so we put forward RGA-CIE - a rule generation algorithm which applies supervised learning with bottom-up strategy. RGA-CIE is a rule generalization process with a heuristic method to decide rule generalization path and laplacian* formula to evaluate the performance of rules. Empirical results show that our approach does be of use in learning of ontology instances.