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Towards Near Real-Time Social Recommendations for the Enterprise

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Innovations in Knowledge Management

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 95))

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Abstract

The widespread use of social platforms in contemporary organizations leads to the generation of large amounts of content shared through various social tools. This information is distributed and often unstructured, making it difficult to fully exploit its value in an enterprise context. While Semantic Web technologies allow for publishing meaningful and structured data, major challenges include: (1) real-time integration of distributed social data, and (2) content personalization to identify relevant pieces of information and present them to users to limit the information overload. We propose to combine Semantic Web technologies with standardized transport protocols, such as XMPP, to provide an efficient and open source layer for aggregation of distributed social data in an enterprise. In addition, we propose a personalisation approach, which is able to provide filtered and personalised access on top of such distributed social data.

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Notes

  1. 1.

    https://www.facebook.com/note.php?note_id=206484249362078.

  2. 2.

    http://facebook.com.

  3. 3.

    See for example http://www.reuters.com/.

  4. 4.

    http://www.w3.org/TR/sparql11-update/.

  5. 5.

    http://www.w3.org/Submission/2008/SUBM-SPARQL-Update-20080715/.

  6. 6.

    http://xmpp.org/rfcs/rfc6120.html, http://xmpp.org/rfcs/rfc6121.html, http://xmpp.org/rfcs/rfc6122.html.

  7. 7.

    http://xmpp.org/extensions/xep-0060.html.

  8. 8.

    see http://xmpp.org/extensions/xep-0124.html.

  9. 9.

    http://xmpp.org/extensions/xep-0060.html.

  10. 10.

    https://github.com/derixmpppubsub/derixmpppubsub/.

  11. 11.

    http://xmpp.org/extensions/xep-0060.html.

  12. 12.

    http://stackexchange.com/.

  13. 13.

    http://stackoverflow.com/.

  14. 14.

    https://creativecommons.org/licenses/by-sa/2.5/.

  15. 15.

    http://www.clearbits.net/creators/146-stack-exchange-data-dump.

  16. 16.

    http://security.stackexchange.com/.

  17. 17.

    http://webapps.stackexchange.com/.

  18. 18.

    http://bicycles.stackexchange.com/.

  19. 19.

    http://www.dbpedia.org/.

  20. 20.

    http://www.w3.org/2004/02/skos/.

  21. 21.

    http://wiki.dbpedia.org/Downloads37.

  22. 22.

    http://www.igniterealtime.org/projects/openfire/.

  23. 23.

    http://www.igniterealtime.org/projects/smack/.

  24. 24.

    http://www.igniterealtime.org/projects/smack/.

  25. 25.

    https://jena.apache.org/documentation/serving_data/index.html.

  26. 26.

    https://jena.apache.org/documentation/tdb/index.html.

  27. 27.

    https://www.mediawiki.org/wiki/API:Opensearch.

  28. 28.

    https://www.mediawiki.org/wiki/API:Search.

  29. 29.

    https://jena.apache.org/documentation/tdb/index.html.

  30. 30.

    https://tomcat.apache.org/.

  31. 31.

    http://www.igniterealtime.org/projects/smack/.

  32. 32.

    https://jena.apache.org/documentation/serving_data/index.html.

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Acknowledgements

This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 and by the ADVANSSE project funded by Cisco Systems.

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Authors and Affiliations

  1. INSIGHT Centre for Data Analytics, NUI Galway, Galway, Ireland

    Benjamin Heitmann, Maciej Dabrowski & Conor Hayes

  2. Cisco Systems, Galway, Ireland

    Keith Griffin

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  1. Benjamin Heitmann
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  2. Maciej Dabrowski
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  3. Conor Hayes
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Correspondence to Benjamin Heitmann .

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Editors and Affiliations

  1. Department of International Business Communication, Copenhagen Business School, Frederiksberg, Denmark

    Liana Razmerita

  2. Sellinger School of Business and Management, Loyola University Maryland, Baltimore, Maryland, USA

    Gloria Phillips-Wren

  3. Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

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Heitmann, B., Dabrowski, M., Hayes, C., Griffin, K. (2016). Towards Near Real-Time Social Recommendations for the Enterprise. In: Razmerita, L., Phillips-Wren, G., Jain, L. (eds) Innovations in Knowledge Management. Intelligent Systems Reference Library, vol 95. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47827-1_9

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  • DOI: https://doi.org/10.1007/978-3-662-47827-1_9

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