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Modeling, Exploring and Recommending Music in Its Complexity

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Knowledge Engineering and Knowledge Management (EKAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10180))

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Abstract

Knowledge models that are currently in-use for describing music metadata are insufficient to express the wealth of complex information about creative works, performances, publications, authors and performers. In this thesis, we aim to propose a method for structuring the music information coming from heterogeneous librarian repositories. In particular, we research and design an appropriate music ontology based on existing models and controlled vocabularies and we implement tools for converting and visualizing the metadata. Moreover, we research how this data can be consumed by end-users, through the development of a web application for exploring the data. We ultimately aim to develop a recommendation system that takes advantage of the richness of the data.

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Notes

  1. 1.

    https://www.loc.gov/marc/.

  2. 2.

    http://www.doremus.org.

  3. 3.

    http://data.doremus.org/ontology.

  4. 4.

    https://github.com/fernanev/ARyTREx.

  5. 5.

    https://github.com/DOREMUS-ANR/marc2rdf.

  6. 6.

    https://github.com/DOREMUS-ANR/overture.

References

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Acknowledgments

I would like to thank my supervisor Raphaël Troncy for his ongoing support in. This work has been partially supported by the French National Research Agency (ANR) within the DOREMUS Project, under grant number ANR-14-CE24-0020.

Author information

Authors and Affiliations

  1. EURECOM, Sophia Antipolis, France

    Pasquale Lisena

Authors
  1. Pasquale Lisena
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Correspondence to Pasquale Lisena .

Editor information

Editors and Affiliations

  1. University of Bologna , Bologna, Italy

    Paolo Ciancarini

  2. University of Bologna , Bologna, Italy

    Francesco Poggi

  3. Stanford University , Palo Alto, California, USA

    Matthew Horridge

  4. Lancaster University , Lancaster, United Kingdom

    Jun Zhao

  5. Garvan Institute of Medical Research , Darlinghurst, New South Wales, Australia

    Tudor Groza

  6. Universidad Politecnica de Madrid , Boadilla del Monte, Spain

    Mari Carmen Suarez-Figueroa

  7. The Open University , Milton Keynes, United Kingdom

    Mathieu d'Aquin

  8. STLab ISTC-CNR , Rome, Italy

    Valentina Presutti

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Lisena, P. (2017). Modeling, Exploring and Recommending Music in Its Complexity. In: Ciancarini, P., et al. Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10180. Springer, Cham. https://doi.org/10.1007/978-3-319-58694-6_41

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  • DOI: https://doi.org/10.1007/978-3-319-58694-6_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58693-9

  • Online ISBN: 978-3-319-58694-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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