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
AbstractIn 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.
|Publication size in sheets||0.55|
|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 English||art market, Semantic web, Linked data, Machine learning, Information retrieval, Alternative investment, Digital humanities|
|Score|| = 0.0, 11-02-2020, BookChapterSeriesAndMatConfByIndicator|
= 0.0, 11-02-2020, BookChapterSeriesAndMatConfByIndicator
|Publication indicators||= 1|
|Citation count*||3 (2020-05-07)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.