High quality domain ontologies are essential for successful employment of semantic Web services. However, their acquisition is difficult and costly, thus hampering the development of this field. In this paper we report on the first stage of research that aims to develop (semi-)automatic ontology learning tools in the context of Web services that can support domain experts in the ontology building task. The goal of this first stage was to get a better understanding of the problem at hand and to determine which techniques might be feasible to use. To this end, we developed a framework for (semi-)automatic ontology learning from textual sources attached to Web services. The framework exploits the fact that these sources are expressed in a specific sublanguage, making them amenable to automatic analysis. We implement two methods in this framework, which differ in the complexity of the employed linguistic analysis. We evaluate the methods in two different domains, verifying the quality of the extracted ontologies against high quality hand-built ontologies of these domains. Our evaluation lead to a set of valuable conclusions on which further work can be based. First, it appears that our method, while tailored for the Web services context, might be applicable across different domains. Second, we concluded that deeper linguistic analysis

An ontology may be decomposed into a layer of binary fact types and a layer of application specific constraints imposed on these fact types. An ontology base is a large set of binary fact types called lexons. This paper presents LexoVis, a lexon visualization tool that addresses the inherent size and scale of ontology bases. LexoVis facilitates the analysis of lexons by providing an ordered visual representation. This representation offers overview and detail by employing the graphical fisheye view. Different ordering and clustering heuristics incorporated in LexoVis lead to insights not explicit in text-based representations of lexons. 1.

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