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


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



Published: 2016 Juni
Herausgeber: Wil M. P. van der Aalst, John Mylopoulos, Sudha Ram, Michael Rosemann, Clemens Szyperski
Buchtitel: Proceedings of 19th Int. Conf. on Business Information Systems 2016 (BIS 2016)
Ausgabe: 255
Reihe: Lecture Notes in Business Information Processing
Seiten: 79--90
Verlag: Springer
Erscheinungsort: Cham

Referierte Veröffentlichung

BibTeX

Kurzfassung
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 recog- nition (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.

ISBN: 978-3-319-39425-1
ISSN: 1865-1348
Download: Media:2016Filipiak-BIS2016.pdf
Weitere Informationen unter: Link
DOI Link: 10.1007/978-3-319-39426-8_7



Forschungsgruppe

Information Service Engineering


Forschungsgebiet