Abstract:

To improve the efficiency and accuracy in choosing concepts and relations of domain ontology, we present an unstructured data based ontology learning model. In the process of extracting the candidate concepts for synthetic word processing, we modified calculation method of frequent item of association rules, and combined with a bitmap to store physically adjacent relationship between the terms after word processing. We filter candidate concepts by calculating areas correlation and areas consistent degree. The association rule credibility and hierarchical clustering methods were used to extract nontaxonomic relations between concepts and classification relationships. Experimental results show that this model is rational in the aspect of domain ontology learning and this algorithm is efficient and accurate in the aspect of extracting concepts and relationships.

Key words: ontology learning, unstructured data, association rules, bitmap, hierarchical clustering