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A Semantic MatchMaking Framework for Volunteering MarketPlaces

  • Johannes Schönböck
  • J. Altmann
  • E. Kapsammer
  • E. Kimmerstorfer
  • B. Pröll
  • W. Retschitzegger
  • W. Schwinger
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 745)

Abstract

Volunteering is an omnipresent cornerstone of our society. Currently, new forms of volunteering like crowd workers, engagement hoppers or patchwork volunteers are arising. This next-generation volunteers more than ever demand for volunteering marketplaces providing adequate MatchMaking capabilities. This paper proposes a semantic MatchMaking framework allowing to compute a ranked list of tasks or volunteers whose profiles match “as closely as possible”. For this, an ontology-based vocabulary is established which explicitly captures the multifaceted nature of profiles for both, tasks and volunteers. Each of these facets is associated with adequate similarity measures and meta information explicitly capturing domain expertise. The feasibility of the approach is demonstrated by a simple example and a first prototype.

Keywords

Volunteering MatchMaking Similarity measure Ontology 

References

  1. 1.
    Becker, M., Laue, R.: A comparative survey of business process similarity measures. Comput. Ind. 63(2), 148–167 (2012)CrossRefGoogle Scholar
  2. 2.
    Bizer, C., et al.: The impact of semantic web technologies on job recruitment processes. In: Wirtschaftsinformatik 2005, pp. 1367–1381. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)CrossRefGoogle Scholar
  4. 4.
    Buttinger, C., Pröll, B., Retschitzegger, W., et al.: JobOlize-headhunting by information extraction in the era of web 2.0. In: Proceedings of 7th IWWOST (2008)Google Scholar
  5. 5.
    Fazel-Zarandi, M., Fox, M.: Semantic matchmaking for job recruitment: an ontology-based hybrid approach. In: Proceedings of 8th Semantic Web Conference (2009)Google Scholar
  6. 6.
    Feng, Y., Bagheri, E., Ensan, F., Jovanovic, J.: The state of the art in semantic relatedness: a framework for comparison. Knowl. Eng. Rev. 32 (2017)Google Scholar
  7. 7.
    Harispe, S., et al.: Semantic similarity from natural language and ontology analysis. Synth. Lect. Hum. Lang. Technol. 8(1), 1–254 (2015)CrossRefGoogle Scholar
  8. 8.
  9. 9.
    ICT Standardisation Work Programme. Integrating Learning Outcomes and Competences. http://www.cetis.org.uk/inloc/Home. Accessed 26 Mar 2017
  10. 10.
    IEEE Standard for Learning Technology - Data Model for Reusable Competency Definitions (2008). https://www.doleta.gov/usworkforce/pdf/2007-ieeecomp.pdf. Accessed 26 Mar 2017
  11. 11.
    Kapsammer, E., Pröll, B., et al.: A reference model for social user profiles: concept & implementation. In: Proceedings of WS on PersDB at 37th VLDB (2011)Google Scholar
  12. 12.
    Kapsammer, E., Pröll, B., Schwinger, W., et al.: iVOLUNTEER - a digital ecosystem for life-long volunteering. In: Proceedings of 19th iiWAS2017. ACM, December 2017Google Scholar
  13. 13.
    Katsarova, I.: European Parliamentary Research Service (2016). https://epthinktank.eu/2016/10/20/volunteering-in-the-eu-plenary-podcast. Accessed 26 Mar 2017
  14. 14.
    Kittur, A., et al.: The future of crowd work. In: Proceedings of the 16th International Conference on Computer Supported Cooperative Work (CSCW), pp. 1301–1318. ACM (2013)Google Scholar
  15. 15.
    Kobsa, A.: Privacy-enhanced personalization. CACM 50(8), 24–33 (2007)CrossRefGoogle Scholar
  16. 16.
    Köpcke, H., Rahm, E.: Frameworks for entity matching: a comparison. Data Knowl. Eng. 69(2), 197–210 (2010)CrossRefGoogle Scholar
  17. 17.
    Lindquist, E.A., Vincent, S., Wanna, J.: Putting Citizens First: Engagement in Policy and Service Delivery for the 21st Century. ANU E Press, Canberra (2013)Google Scholar
  18. 18.
    Lv, H., Zhu, B.: Skill ontology-based semantic model and its matching algorithm. In: Proceedings of 7th International Conference on CAIDCD. ACM (2006)Google Scholar
  19. 19.
    Martinez-Gil, J., Paoletti, A.L., Schewe, K.-D.: A smart approach for matching, learning and querying information from the human resources domain. In: East European Conference on ADBIS, pp. 157–167. Springer, Heidelberg (2016)Google Scholar
  20. 20.
    Miranda, S., Orciuoli, F., Loia, V., Sampson, D.: An ontology-based model for competence management. Data Knowl. Eng. 107, 51–66 (2017)CrossRefGoogle Scholar
  21. 21.
    Otero-Cerdeira, L., Rodríguez-Martínez, F.J., Gómez-Rodríguez, A.: Ontology matching: a literature review. Expert Syst. Appl. 42(2), 949–971 (2015)CrossRefGoogle Scholar
  22. 22.
    Retschitzegger, W., et al.: Making workflows situation aware - an ontology-driven framework for spatial systems. In: Proceedings of 13th iiWAS2011, pp. 182–188 (2011)Google Scholar
  23. 23.
    Rifón, L.A.: Standardising competency definitions for engineering education. In: IEEE Global Engineering Education Conference (EDUCON), pp. 52–58 (2011)Google Scholar
  24. 24.
    Schönböck, J., et al.: A survey on volunteer management systems. In: Proceedings of 49th HICSS, pp. 767–776. IEEE (2016)Google Scholar
  25. 25.
    Schwinger, W., et al.: A survey on web modeling approaches for ubiquitous web applications. IJWIS 4(3), 234–305 (2008)Google Scholar
  26. 26.
    Tarus, J., et al.: Knowledge-based recommendation: a review of ontology-based recommender systems. Artif. Intell. Rev. 1–28 (2017)Google Scholar
  27. 27.
    UN Volunteers. State of the World’s Volunteerism Report (2015). http://www.volunteeractioncounts.org. Accessed 26 Mar 2017

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Johannes Schönböck
    • 1
  • J. Altmann
    • 1
  • E. Kapsammer
    • 2
  • E. Kimmerstorfer
    • 2
  • B. Pröll
    • 2
  • W. Retschitzegger
    • 2
  • W. Schwinger
    • 2
  1. 1.Upper Austrian University of Applied SciencesHagenbergAustria
  2. 2.Johannes Kepler UniversityLinzAustria

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