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Browsing and Navigation in Semantically Rich Spaces

Experiences with Magpie Applications
  • Martin DzborEmail author
  • Enrico Motta
  • Laurian Gridinoc
Chapter
Part of the International Handbooks on Information Systems book series (INFOSYS)

Summary

Semantic Web is a medium for knowledge exchange, where knowledge produced by one agent is consumed by another agent who may extend or modify it. Semantic Web also affords novel opportunities for acquiring knowledge – including approaches favoring automated selection, reuse and integration of external, just-intimegathered semantic resources. As semantic resources are no longer specifically developed for a single purpose, their re-contextualization within other web resources (e.g., web pages) is becoming a more pressing challenge. In this chapter, we look at the case when external semantic resources discovered in the web-sized corpus arere-contextualized to enhance the user experience of an arbitrary web content visited by a particular user. We first review different approaches showcasing different facets of semantic browsing and define the notion of ‘semantic browsing’ in general terms. Next, we share our experiences with Magpie, an in-house semantic web browsing framework, and illustrate new functional features such a semantically-enriched browsing tool may offer on the example of introducing additional user interaction modalities and developing a capability to work with multiple background knowledge models simultaneously. In the discussion we re-visit the defining tenets of ‘semantic browsing’ and look at how the reuse of just-in-time discovered and applied semantic resources really addresses the issue of enabling the user to re-contextualize semantic data for the purposes of text analysis, data interpretation, relationship discovery, and knowledge validation.

Keywords

Semantic Content Semantic Annotation Semantic Data Semantic Service Semantic Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors were supported by the climateprediction.net, Dot.Kom, KnowledgeWeb, Advanced Knowledge Technologies (AKT), OpenKnowledge and NeOn projects. climateprediction.net was sponsored by the UK Natural Environment Research Council and UK Department of Trade e-Science Initiative. Dot.Kom (Designing Adaptive Information Extraction from Text for Knowledge Management) by the IST Framework 5 (grant IST-2001-34038). KnowledgeWeb was an IST Framework 6 Network of Excellence (grant FP6-507482), AKT an Interdisciplinary Research Collaboration (IRC) sponsored by the UK Engineering and Physical Sciences Research Council by grant GR/N15764/01, and OpenKnowledge and NeOn are Framework 6 research projects partly supported by grants IST-2005-027253 and IST-2005-027595.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Knowledge Media InstituteThe Open UniversityMilton KeynesUK

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