Advertisement

Improving the Quality of Art Market Data Using Linked Open Data and Machine Learning

  • Dominik FilipiakEmail author
  • Agata Filipowska
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 263)

Abstract

Among numerous research studies devoted to art markets, very little attention is given to the quality of the data. Availability of a decent amount of observations is a problem in many fields; the art market is no different, especially in Poland. Therefore, it constitutes a severe obstacle in explaining the market behaviour. The use of Linked Open Data and Machine Learning can pave the way to improve the quality of data and enrich results of other art market research as a consequence, such as building indices. This paper is an outline of the method for combining such fields and summarises effort already made to achieve that.

Keywords

Art market Data science Machine learning Linked open data 

References

  1. 1.
    Plattner, S.: A most ingenious paradox: the market for contemporary fine art. Am. Anthropolog. 100(2), 482–493 (1998)CrossRefGoogle Scholar
  2. 2.
    Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., Auer, S.: Quality assessment for linked data: a survey. Semant. Web 7(1), 63–93 (2015)CrossRefGoogle Scholar
  3. 3.
    Wand, Y., Wang, R.Y.: Anchoring data quality dimensions in ontological foundations. Commun. ACM 39(11), 86–95 (1996)CrossRefGoogle Scholar
  4. 4.
    Pipino, L.L., Lee, Y.W., Wang, R.Y.: Data quality assessment. Commun. ACM 45(4), 211–218 (2002)CrossRefGoogle Scholar
  5. 5.
    Borowski, K., Kosmala, W.: The contemporary art market in Poland - paintings. J. Manag. Financ. Sci. 17(15), 63–80 (2014)Google Scholar
  6. 6.
    Witkowska, D., Kompa, K.: Constructing hedonic art price indexes for the Polish painting market using direct and indirect approaches. AESTIMO, The IEB Int. J. Financ. 10, 2–25 (2015)CrossRefGoogle Scholar
  7. 7.
    Witkowska, D., Lucińska, A.: Hedoniczny indeks cen obrazów sprzedanych na polskim rynku aukcyjnym w latach 2007–2013. Zeszyty Naukowe Uniwersytetu Szczecińskiego. Finanse. Rynki finansowe. Ubezpieczenia (75 Rynek kapitałowy: skuteczne inwestowanie), pp. 515–527 (2015)Google Scholar
  8. 8.
    Ginsburgh, V., Mei, J., Moses, M.: The computation of prices indices. In: Handbook of the Economics of Art and Culture, vol. 1. pp. 947–979. Elsevier (2006)Google Scholar
  9. 9.
    Mei, J., Moses, M.: Art as an Investment and the Underperformance of Masterpieces (2002)Google Scholar
  10. 10.
    Renneboog, L., Spaenjers, C.: Buying beauty: on prices and returns in the art market. Manag. Sci., 1–33, April 2012Google Scholar
  11. 11.
    Locatelli-Biey, M., Zanola, R.: The sculpture market: an adjacent year regression index. J. Cult. Econ. 26, 65–78 (2002)CrossRefGoogle Scholar
  12. 12.
    Kräussl, R., Wiehenkamp, C.: A call on art investments. Rev. Deriv. Res. 15(1), 1–23 (2011)CrossRefGoogle Scholar
  13. 13.
    Hartig, O., Bizer, C., Freytag, J.-C.: Executing SPARQL queries over the web of linked data. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 293–309. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-04930-9_19 CrossRefGoogle Scholar
  14. 14.
    Paulheim, H., Ristoski, P., Mitichkin, E., Bizer, C.: Data mining with background knowledge from the web (2014)Google Scholar
  15. 15.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)Google Scholar
  16. 16.
    Elgammal, A.M., Saleh, B.: Quantifying creativity in art networks. CoRR abs/1506.00711 (2015)Google Scholar
  17. 17.
    Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28(1), 75–105 (2004)Google Scholar
  18. 18.
    Filipiak, D., Filipowska, A.: Towards data-oriented analysis of the art market. Financ. Internet Q. 12(1), 21–31 (2016)Google Scholar
  19. 19.
    Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia - a large-scale, multilingual knowledge base extracted from wikipedia. Semant. Web J. 6, 167–195 (2014)Google Scholar
  20. 20.
    Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, HotCloud 2010, p. 10. USENIX Association, Berkeley (2010)Google Scholar
  21. 21.
    Filipiak, D., Filipowska, A.: DBpedia in the art market. In: Abramowicz, W. (ed.) BIS 2015. LNBIP, vol. 228, pp. 321–331. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-26762-3_28 CrossRefGoogle Scholar
  22. 22.
    Filipiak, D., Wȩcel, K., Filipowska, A.: Semantic annotation to support description of the art market. In: 11th International Conference on Semantic Systems, SEMANTiCS 2015, vol. 1481, pp. 51–54. CEUR-WS (2015)Google Scholar
  23. 23.
    Filipiak, D., Agt-Rickauer, H., Hentschel, C., Filipowska, A., Sack, H.: Quantitative analysis of art market using ontologies, named entity recognition and machine learning: a case study. In: Abramowicz, W., Alt, R., Franczyk, B. (eds.) BIS 2016. LNBIP, vol. 255, pp. 79–90. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-39426-8_7 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Departament of Information SystemsPoznań University of Economics and BusinessPoznańPoland

Personalised recommendations