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Personalized Information Access Using Semantic Knowledge

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Smart Information Systems

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

Handling the amount of information on the Web, known as the information overload problem, requires tremendous effort. One approach that relieves the user from this burden is offering personalized information access. Systems that adopt to users’ preferences are called adaptive systems. Based on a user profile containing details about the users’ preferences, the system adapts its content or the user interface to the user. In this chapter, we present a personalized news information system, providing users with entertainment news tailored to their needs. Using semantic technologies, the time to learn user preferences is reduced to a few interactions. We present the system in detail, and present an evaluation showing the benefits coming with the semantic approach.

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Notes

  1. 1.

    http://news.google.com/, search conducted on September 19th, 2014.

  2. 2.

    RDFa (or Resource Description Framework—in—attributes) is a W3C Recommendation that allows to embed rich metadata within Web documents.

  3. 3.

    http://www.neofonie.de/.

  4. 4.

    http://freebase.com/.

  5. 5.

    http://dbpedia.org/.

  6. 6.

    http://www.wikipedia.org/.

  7. 7.

    http://developers.facebook.com/docs/opengraph/.

  8. 8.

    http://musicbrainz.org/.

  9. 9.

    http://www.foaf-project.org/.

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Acknowledgments

This work was funded by the Federal Ministry of Economic Affairs and Energy (BMWi) under funding reference number KF2392305KM0.

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Authors and Affiliations

  1. Technische Universität Berlin, Berlin, Germany

    Till Plumbaum & Andreas Lommatzsch

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  1. Till Plumbaum
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Correspondence to Till Plumbaum .

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  1. Technische Universität Berlin, Germany

    Frank Hopfgartner

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Plumbaum, T., Lommatzsch, A. (2015). Personalized Information Access Using Semantic Knowledge. In: Hopfgartner, F. (eds) Smart Information Systems. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-14178-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-14178-7_7

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