Contrasting Ontology Modeling with Correlation Rules for Delivery Applications

With the increasing importance of knowledge management, variant management and the ever-growing quantity of data, ontologies emerged as a form of knowledge representation, especially in the field of technical communication for modelling metadata and to create correlations between them. In the area of delivery applications, the deliverable information objects receive a certain intelligence by semantic metadata. It is expected, that ontologies offer a higher level of intelligence which could lead to an improvement in classification, connection and delivery possibilities of content. On the contrary, creating those complex ontologies requires a time-consuming effort. Thus, the question arises, whether their use offers a decisive added benefit or if alternatives, such as untyped correlations, should be preferred. In that case, the concept of Semantic Correlation Rules can offer an opportunity to derive advantages from ontologies: By defining which classifications are connected to others, it is possible to present content tailored to user-specific information requirements. By developing use cases, we aim to evaluate the required level of intelligence of the metadata resulting from its modeling method to achieve this goal