Semi-automatic Ontology Construction based on Patterns

This thesis aims to improve the ontology engineering process, by providing better semiautomatic support for constructing ontologies and introducing knowledge reuse through ontology patterns. The thesis introduces a typology of patterns, a general framework of pattern-based semi-automatic ontology construction called OntoCase, and provides a set of methods to solve some specific tasks within this framework. Experimental results indicate some benefits and drawbacks of both ontology patterns, in general, and semi-automatic ontology engineering using patterns, the OntoCase framework, in particular. The general setting of this thesis is the field of information logistics, which focuses on how to provide the right information at the right moment in time to the right person or organisation, sent through the right medium. The thesis focuses on constructing enterprise ontologies to be used for structuring and retrieving information related to a certain enterprise. This means that the ontologies are quite 'light weight' in terms of logical complexity and expressiveness. Applying ontology content design patterns within semi-automatic ontology construction, i.e. ontology learning, is a novel approach. The main contributions of this thesis are a typology of patterns together with a pattern catalogue, an overall framework for semi-automatic patternbased ontology construction, specific methods for solving partial problems within this framework, and evaluation results showing the characteristics of ontologies constructed semiautomatically based on patterns. Results show that it is possible to improve the results of typical existing ontology learning methods by selecting and reusing patterns. OntoCase is able to introduce a general top-structure to the ontologies, and by exploiting background knowledge, the ontology is given a richer structure than when patterns are not applied.