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

Part of the Lecture Notes in Business Information Processing book series (LNBIP,volume 255)


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.


  • Art market
  • Semantic web
  • Linked data
  • Machine learning
  • Information retrieval
  • Alternative investment
  • Digital humanities

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Correspondence to Dominik Filipiak .

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Filipiak, D., Agt-Rickauer, H., Hentschel, C., Filipowska, A., Sack, H. (2016). Quantitative Analysis of Art Market Using Ontologies, Named Entity Recognition and Machine Learning: A Case Study. In: Abramowicz, W., Alt, R., Franczyk, B. (eds) Business Information Systems. BIS 2016. Lecture Notes in Business Information Processing, vol 255. Springer, Cham.

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