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Abstract

This entry introduces ontologies as a potential "silver bullet" for knowledge management, enterprise application integration, and e-commerce. Ontologies enable knowledge sharing and knowledge reuse. The degree to which an ontology is machine-understandable, its formality, is determined by the language used for the specification of the ontology. There exists a trade-off between the expressiveness of an ontology language and the modeling support it provides for the ontology developer. This entry also describes how different knowledge representation formalisms, together with the Web languages XML and RDF, have influenced the development of the Web ontology language OWL.

Content

Digital unter: http://dx.doi.org/10.1081/E-ELIS3-120039479. Vgl.: http://www.tandfonline.com/doi/book/10.1081/E-ELIS3.

Language

e

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Abstract

Hierarchical classifications, thesauri, and informal taxonomies are likely the most valuable input for creating, at reasonable cost, non-toy ontologies in many domains. They contain, readily available, a wealth of category definitions plus a hierarchy, and they reflect some degree of community consensus. However, their transformation into useful ontologies is not as straightforward as it appears. In this paper, we show that (1) it often depends on the context of usage whether an informal hierarchical categorization schema is a classification, a thesaurus, or a taxonomy, and (2) present a novel methodology for automatically deriving consistent RDF-S and OWL ontologies from such schemas. Finally, we (3) demonstrate the usefulness of this approach by transforming the two e-business categorization standards eCl@ss and UNSPSC into ontologies that overcome the limitations of earlier prototypes. Our approach allows for the script-based creation of meaningful ontology classes for a particular context while preserving the original hierarchy, even if the latter is not a real subsumption hierarchy in this particular context. Human intervention in the transformation is limited to checking some conceptual properties and identifying frequent anomalies, and the only input required is an informal categorization plus a notion of the target context. In particular, the approach does not require instance data, as ontology learning approaches would usually do.

Language

e