Peter Scheir

A Network Model Approach to Retrieval in the Semantic Web

While it is agreed that semantic enrichment of resources would lead to better search results, at present the low coverage of resources on the Web with semantic information presents a major hurdle in realizing the vision of search on the Semantic Web. To address this problem, we investigate how to improve retrieval performance in settings where resources are sparsely annotated with semantic information. Techniques from soft computing are employed to find relevant material that was not originally annotated with the concepts used in a query. We present an associative retrieval model for the Semantic Web and evaluate if and to what extent the use of associative retrieval techniques increases retrieval performance. The evaluation of new retrieval paradigms, such as retrieval based on technology for the Semantic Web, presents an additional challenge since no off-the-shelf test corpora exist. Hence, we give a detailed description of the approach taken to evaluate the information retrieval service we have built.

Deploying Semantic Web Technologies for Work Integrated Learning in Industry - A Comparison: SME vs. Large Sized Company

Modern businesses operate in a rapidly changing environment. Continuous learning is an essential ingredient in order to stay competitive in such environments. The APOSDLE system utilizes semantic web technologies to create a generic system for supporting knowledge workers in different domains to learn work. Since APOSDLE relies on three interconnected semantic models to achieve this goal, the question on how to efficiently create high-quality semantic models has become one of the major research challenges. On the basis of two concrete examples-namely deployment of such a learning system at EADS, a large corporation, and deployment at ISN, a network of SMEs-we report in detail the issues a company has to face, when it wants to deploy a modern learning environment relying on semantic web technology.

Knowledge Services for Work-Integrated Learning

In order to support work-integrated learning scenarios task- and competency-aware knowledge services are needed. In this paper we introduce three key knowledge services of the APOSDLE system and illustrate how they interact. The context determination daemon observes user interactions and infers the current work task of the user. The user profile service uses the identified work tasks to determine the competences of the user. And finally, the associative retrieval service utilizes both the current work task and the inferred competences to identify relevant (learning) content. All of these knowledge services improve through user feedback.

ARS/SD: An Associative Retrieval Service for the Semantic Desktop

While it is agreed that semantic enrichment of resources would lead to better search results, at present the low coverage of resources on the web with semantic information presents a major hurdle in realizing the vision of search on the Semantic Web. To address this problem we investigate how to improve retrieval performance in a setting where resources are sparsely annotated with semantic information. We suggest employing techniques from associative information retrieval to find relevant material, which was not originally annotated with the concepts used in a query. We present an associative retrieval service for the Semantic Desktop and evaluate if the use of associative retrieval techniques increases retrieval performance.