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A Semantic Web Approach for Visualization-Based News Analytics

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Knowledge Management in Organizations (KMO 2014)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 185))

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Abstract

In order to understand news, dependency patterns between objects in (economic) news items have to be detected. We propose a framework which makes it possible to discover these patterns, and support the observations with statistical analysis. Based on these patterns, alerts can be generated based on emerging news. These alerts can then be used to manage (equity) portfolios. We test our framework based on historical data. The tests show statistically significant results supporting the idea that it is possible to discover such dependency patterns between objects in news items.

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Author information

Authors and Affiliations

  1. Erasmus School of Economics, Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands

    Maarten Jongmans, Viorel Milea & Flavius Frasincar

Authors
  1. Maarten Jongmans
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  2. Viorel Milea
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  3. Flavius Frasincar
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Corresponding author

Correspondence to Viorel Milea .

Editor information

Editors and Affiliations

  1. Staffordshire University, Staffordshire, United Kingdom

    Lorna Uden

  2. Universidad Santa Maria, Santiago, Chile

    Darcy Fuenzaliza Oshee

  3. National University of Kaohsiung, Kaohsiung City, Taiwan

    I-Hsien Ting

  4. Universidad Santa Maria, Santiago, Chile

    Dario Liberona

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© 2014 Springer International Publishing Switzerland

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Jongmans, M., Milea, V., Frasincar, F. (2014). A Semantic Web Approach for Visualization-Based News Analytics. In: Uden, L., Fuenzaliza Oshee, D., Ting, IH., Liberona, D. (eds) Knowledge Management in Organizations. KMO 2014. Lecture Notes in Business Information Processing, vol 185. Springer, Cham. https://doi.org/10.1007/978-3-319-08618-7_20

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  • DOI: https://doi.org/10.1007/978-3-319-08618-7_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08617-0

  • Online ISBN: 978-3-319-08618-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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