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Stern Hermann, Kaiser Rene, Hofmair P., Lindstaedt Stefanie , Scheir Peter, Kraker Peter

Content Recommendation in APOSDLE using the Associative Network

Journal of Universal Computer Science, 2010

One of the success factors of Work Integrated Learning (WIL) is to provide theappropriate content to the users, both suitable for the topics they are currently working on, andtheir experience level in these topics. Our main contributions in this paper are (i) overcomingthe problem of sparse content annotation by using a network based recommendation approachcalled Associative Network, which exploits the user context as input; (ii) using snippets for notonly highlighting relevant parts of documents, but also serving as a basic concept enabling theWIL system to handle text-based and audiovisual content the same way; and (iii) using the WebTool for Ontology Evaluation (WTE) toolkit for finding the best default semantic similaritymeasure of the Associative Network for new domains. The approach presented is employed inthe software platform APOSDLE, which is designed to enable knowledge workers to learn atwork.

Scheir Peter, Granitzer Michael, Lindstaedt Stefanie , Hofmair P.

The OntologyMapper plug-in: Supporting Semantic Annotation of Text-Documents by Classification

Semantic Systems From Vision to Applications - Proceedings of the SEMANTICS 2006, Vienna, Austria, November 28-30, 2006, Österreichische Computer Gesellschaft, Wien, 2006

In this contribution we present a tool for annotating documents, which are used for workintegratedlearning, with concepts from an ontology. To allow for annotating directly whilecreating or editing an ontology, the tool was realized as a plug-in for the ontology editor Protégé.Annotating documents with semantic metadata is a laborious task, most of the time knowledgerepresentations are created independently from the resources that should be annotated andadditionally in most work environments a high number of documents exist. To increase theefficiency of the person annotating, in our tool the process of assigning concepts to text-documentsis supported by automatic text-classification.
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