Preface
Expressive ontologies are an important prerequisite for a growing number of knowledge-intensive applications nowadays emerging from semantic web research. These applications, many of them originating in fast growing areas such as bioinformatics or medicine, demand for rich axiomatizations of huge and conceptually complex domains of interest. However, the sheer size and complexity of the required ontologies make high demands on the modeling capabilities of human ontology engineers, and modeling errors as well as unforseen logical consequences frequently hinder the practical application of the acquired knowledge. But even though any kind of automatic support in the acquisition and evaluation of expressive ontologies would be highly desirable, ontology learning so far has largely concentrated on the generation of relatively lightweight ontologies.
This PhD thesis presents a set of novel approaches to facilitate the semi-automatic generation and maintenance of expressive OWL DL ontologies. It describes the benefits and applications of logically complex axiomatizations while at the same time highlighting some of the greatest challenges for future ontology learning – including aspects of user interaction as well as uncertainty and inconsistency handling. The methods proposed by this thesis aim to combine the strengths of lexical and logical knowledge acquisition techniques. Their implementations, based on machine learning, natural language processing, formal concept analysis and OWL reasoning, demonstrate the practical feasibility of automatic support in engineering expressive ontologies. Particular emphasis is put on various aspects of ontology quality such as formal correctness, logical consistency and completeness.
Altogether, this thesis takes a first, yet important, step towards the semi-automatic generation of logically complex axiomatizations. Our experiments give raise to the hope that we can finally widen the knowledge acquisition bottleneck for reasoning-based applications.
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