Quantitative Analysis of Art Market Using Ontologies, Named Entity Recognition and Machine Learning: A Case Study

Dominik Filipiak , Henning Agt-Rickauer , Christian Hentschel , Agata Filipowska , Herald Sack


In the paper we investigate new approaches to quantitative art market research, such as statistical analysis and building of market indices. An ontology has been designed to describe art market data in a unified way. To ensure the quality of information in the knowledge base of the ontology, data enrichment techniques such as named entity recognition (NER) or data linking are also involved. By using techniques from computer vision and machine learning, we predict a style of a painting. This paper comes with a case study example being a detailed validation of our approach.
Author Dominik Filipiak (UEP)
Dominik Filipiak,,
- Poznań University of Economics and Business
, Henning Agt-Rickauer - Hasso-Plattner Institut, Potsdam, Germany
Henning Agt-Rickauer,,
, Christian Hentschel - Hasso-Plattner Institut, Potsdam, Germany
Christian Hentschel,,
, Agata Filipowska (WIiGE / KIE)
Agata Filipowska,,
- Department of Information Systems
, Herald Sack - Hasso-Plattner Institut, Potsdam, Germany
Herald Sack,,
Publication size in sheets0.55
Book Abramowicz Witold, Alt Rainer, Franczyk Bogdan (eds.): Business Information Systems : 19th International Conference, BIS 2016, Proceedings, Lecture Notes in Business Information Processing, vol. 255, 2016, Springer International Publishing, ISBN 978-3-319-39425-1, [978-3-319-39426-8], 450 p., DOI:10.1007/978-3-319-39426-8
Keywords in Englishart market, Semantic web, Linked data, Machine learning, Information retrieval, Alternative investment, Digital humanities
URL https://link.springer.com/chapter/10.1007/978-3-319-39426-8_7#Abs1
Languageen angielski
Score (nominal)15
ScoreMinisterial score = 0.0, 11-02-2020, BookChapterSeriesAndMatConfByIndicator
Ministerial score (2013-2016) = 0.0, 11-02-2020, BookChapterSeriesAndMatConfByIndicator
Publication indicators WoS Citations = 1
Citation count*4 (2021-07-24)
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