Transforming Existing Knowledge Models

Diverse types of structured domain models are nowadays in use in various contexts. On the one hand there are generic models, especially domain ontologies, which are typically used in applications with artificial intelligence (reasoning) flavor; on the other hand there are more specific models that only come to use in areas like software engineering or business analysis. Furthermore, the discipline of information extraction has invented very specific knowledge models called extraction ontologies, whose purpose is to help extract and semantically annotate textual data. In this paper we present a method of authoring extraction ontologies (more specifically, their abstract constituents called presentation ontologies) via reusing different types of other knowledge models, especially domain ontologies and UML models. Our priority is to maintain consistency between extracted data and those prior models.