Reasoning-Supported Quality Assurance for Knowledge Bases

The increasing application of ontology reuse and automated knowledge acquisition tools in ontology engineering brings about a shift of development efforts from knowledge modeling towards quality assurance. When ontology reuse or automatic knowledge acquisition are applied, accuracy and conciseness are the two most typical quality problems. Yet, despite the high practical importance, there has been a substantial lack of support for essential quality assurance activities concerning these two quality dimensions. In this thesis, we make a significant step forward in ontology engineering by developing a support for two such essential quality assurance activities. We develop a sophisticated framework and the corresponding tool support for partially automating the inspection of ontologies with respect to accuracy. This is a significant contribution in the field of ontology engineering, since manual inspection of ontologies, not replaceable by ontology debugging or constraint formalization in professional ontology engineering projects, is one of the most costly alternatives in quality assurance due to the high amount of required user interaction. The framework is based on the assumption that the deductive closure of the correct axioms must be disjoint from the set of incorrect axioms, which holds for all standardized ontology languages with formal semantics. Given this general assumption, we employ reasoning in order to reduce the number of decisions that have to be taken by a domain expert in order to complete the inspection. Due to its generality, the framework allows for a maximum automation achieved by reasoning for a wide range of ontology modeling languages and for a flexible choice of initial constraints applying to the ontology. Since the order of inspection has an impact on the effectiveness of the reasoningbased support, we further propose and compare various axiom ranking techniques used to determine a beneficial order of inspection. These ranking heuristics are based on the expected accuracy ratio of an ontology and aim at choosing axioms with the highest number of subsequent automatic evaluations. In order to deliberate the user from having to provide an estimate of the accuracy ratio in advance, we show that this estimate can effectively be learned on-the-fly over the course

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