Semantic Integration of Web Data for International Investment Decision Support

  • Boyan Simeonov
  • Vladimir Alexiev
  • Dimitris LiparasEmail author
  • Marti Puigbo
  • Stefanos Vrochidis
  • Emmanuel Jamin
  • Ioannis Kompatsiaris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9934)


Given the current economic situation and the financial crisis in many European countries, Small and Medium Enterprises (SMEs) have found internationalisation and exportation of their products as the main way out of this crisis. In this paper, we provide a decision support system that semantically aggregates information from many heterogeneous web resources and provides guidance to SMEs for their potential investments. The main contributions of this paper are the introduction of SME internationalisation indicators that can be considered for such decisions, as well as the novel decision support system for SME internationalisation based on inference over semantically integrated data from heterogeneous web resources. The system is evaluated by SME experts in realistic scenarios in the section of dairy products.


Decision support Indicators Heterogeneous web resources SME internationalisation Semantic integration 



This work was supported by the project MULTISENSOR (FP7-610411), funded by the European Commission.


  1. 1.
    Power, D.J.: Decision support systems: concepts and resources for managers. Stud. Inform. Control 11(4), 349–350 (2002)Google Scholar
  2. 2.
    Power, D.J., Sharda, R.: Model-driven decision support systems: concepts and research directions. Decis. Support Syst. 43(3), 1044–1061 (2007)CrossRefGoogle Scholar
  3. 3.
    Power, D.J.: What are examples of decision support systems in global enterprises? DSS News, 7(7) (2006)Google Scholar
  4. 4.
    Power, D.J.: Understanding data-driven decision support systems. Inform. Syst. Manage. 25(2), 149–154 (2008)CrossRefGoogle Scholar
  5. 5.
    Blomqvist, E.: The use of Semantic Web technologies for decision support–a survey. Semant. Web 5(3), 177–201 (2014)Google Scholar
  6. 6.
    Pontz, C., Power D.J.: Building an Expert Assistance System for Examiners (EASE) at the Pennsylvania Department of Labor and Industry (2002)Google Scholar
  7. 7.
    Jafarpour, B., Abidi, S.R., Abidi, S.S.R.: Exploiting OWL reasoning services to execute ontologically-modeled clinical practice guidelines. In: Peleg, M., Lavrač, N., Combi, C. (eds.) AIME 2011. LNCS, vol. 6747, pp. 307–311. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Sanchez, E., Toro, C., Carrasco, E., Bueno, G., Parra, C., Bonachela, P., Graña, M., Guijarro, F.: An architecture for the semantic enhancement of clinical decision support systems. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part II. LNCS, vol. 6882, pp. 611–620. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Kim, H.J., Kim, W., Lee, M.: Semantic Web Constraint Language and its application to an intelligent shopping agent. Decis. Support Syst. 46(4), 882–894 (2009)CrossRefGoogle Scholar
  10. 10.
    Huang, S.L., Tsai, Y.H.: Designing a cross-language comparison-shopping agent. Decis. Support Syst. 50(2), 428–438 (2011)CrossRefGoogle Scholar
  11. 11.
    Wanner, L., Rospocher, M., Vrochidis, S., Johansson, L., Bouayad-Aghae, N., Casamayor, G., Karppinen, A., Kompatsiaris, I., Millee, S., Moumtzidou, A., Serafini, L.: Ontology-centered environmental information delivery for personalized decision support. Expert Syst. Appl. 42(12), 5032–5046 (2015)CrossRefGoogle Scholar
  12. 12.
    Tseng, C.C., Gmytrasiewicz, P.J.: Real time decision support system for portfolio management. In Proceedings of the 35th Annual Hawaii International Conference on System Sciences, HICSS, pp. 1348–1356. IEEE (2002)Google Scholar
  13. 13.
    Casanova, I.J.: Portfolio investment decision support system based on a fuzzy inference system. In: Madani, K., Dourado Correia, A., Rosa, A., Filipe, J. (eds.) Computational Intelligence. SCI, vol. 399, pp. 183–196. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Weber, B.W.: Financial DSS: Systems for supporting investment decisions. Handbook on Decision Support Systems 2, pp. 419–442. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Cyganiak, R., Reynolds, D.: The RDF Data Cube Vocabulary. W3C Recommendation, 16 January 2014 (2014).
  16. 16.
    Statistical Data and Metadata eXchange. Official site for the SDMX community. Accessed Apr 2016
  17. 17.
    Capadisli, S., Auer, S., Riedl, R.: Towards linked statistical data analysis. In: Proceedings of the 1st International Workshop on Semantic Statistics, Sydney, Australia, 11 October 2013. CEUR vol. 1549 (2013).
  18. 18.
    Alexiev, V., Breu, M., de Bruijn, J., Fensel, D., Lara, R., Lausen, H.: Information Integration with Ontologies: Experiences from an Industrial Showcase. Wiley, New York (2005). ISBN 978-0-470-01048-8Google Scholar
  19. 19.
    Gearon, P., Passant, A., Polleres, A.: SPARQL 1.1 Update, W3C Recommendation 21 March 2013 (2013).
  20. 20.
    Heise, N., Wagner, T., Eckhoff, M., Vrochidis, S., Peleja, F.: MULTISENSOR Second Prototype Evaluation Report (2015)

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Boyan Simeonov
    • 1
  • Vladimir Alexiev
    • 1
  • Dimitris Liparas
    • 2
    Email author
  • Marti Puigbo
    • 3
  • Stefanos Vrochidis
    • 2
  • Emmanuel Jamin
    • 4
  • Ioannis Kompatsiaris
    • 2
  1. 1.Ontotext CorpSofiaBulgaria
  2. 2.Information Technologies Institute, Centre for Research and Technology HellasThermi-ThessalonikiGreece
  3. 3.PIMECBarcelonaSpain
  4. 4.EverisBarcelonaSpain

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