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Open Cultural Heritage Data in University Programming Courses

  • Tabea TietzEmail author
  • Harald Sack
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11762)

Abstract

Cultural heritage data are not only an important research subject for the semantic web community, but also provide interesting material for practical programming courses in universities. In this paper, four projects created by master students at Karlsruhe Institute of Technology (KIT) show how open cultural heritage data can be used to develop creative and ambitious applications which improve the students’ knowledge and experience working with semantic web technologies, linked data, natural language processing techniques and machine learning. Furthermore, challenges and lessons learned are discussed.

Keywords

Education Cultural heritage Semantic web Linked data Data exploration Machine learning Natural language processing 

Notes

Acknowledgement

We would like to thank all seminar students and tutors, who invested a great amount of work to make each project a successful one, and the Coding da Vinci initiative for providing the extensive amount of data.

References

  1. 1.
    Coding da Vinci (2014). Accessed 19 Mar 2019. https://codingdavinci.de/
  2. 2.
    Course Projects. Accessed 23 Apr 2019. https://ise-fizkarlsruhe.github.io/CourseProjects2019
  3. 3.
    Bavarian St. Library. Accessed 19 Mar 2019. https://www.bsb-muenchen.de/
  4. 4.
    Hessian St. Archives. Accessed 19 Mar 2019. https://landesarchiv.hessen.de/
  5. 5.
    Städel Museum. Accessed 19 Mar 2019. https://www.staedelmuseum.de/en
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    Dragoni, M., Tonelli, S., Moretti, G.: A knowledge management architecture for digital cultural heritage. JOCCH 10(3), 15 (2017)CrossRefGoogle Scholar
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    Simou, N., Chortaras, A., Stamou, G., Kollias, S.: Enriching and publishing cultural heritage as linked open data. Mixed Reality and Gamification for Cultural Heritage, pp. 201–223. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-49607-8_7CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Karlsruhe Institute of Technology, Institute AIFBKarlsruheGermany
  2. 2.FIZ Karlsruhe – Leibniz Institute for Information InfrastructureEggenstein-LeopoldshafenGermany

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