DOREMUS: A Graph of Linked Musical Works

  • Manel Achichi
  • Pasquale Lisena
  • Konstantin TodorovEmail author
  • Raphaël Troncy
  • Jean Delahousse
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11137)


Three major French cultural institutions—the French National Library (BnF), Radio France and the Philharmonie de Paris—have come together in order to develop shared methods to describe semantically their catalogs of music works and events. This process comprises the construction of knowledge graphs representing the data contained in these catalogs following a novel agreed upon ontology that extends CIDOC-CRM and FRBRoo, the linking of these graphs and their open publication on the web. A number of specialized tools that allow for the reproduction of this process are developed, as well as web applications for easy access and navigation through the data. The paper presents one of the main outcomes of this project—the DOREMUS knowledge graph, consisting of three linked datasets describing classical music works and their associated events (e.g., performances in concerts). This resource fills an important gap between library content description and music metadata. We present the DOREMUS pipeline for lifting and linking the data, the tools developed for these purposes, as well as a search application allowing to explore the data.



This work has been partially supported by the French National Research Agency within the DOREMUS project, under grant ANR-14-CE24-0020.


  1. 1.
    Marden, J., Li-Madeo, C., Whysel, N., Edelstein, J.: Linked open data for cultural heritage: evolution of an information technology. In: ICDC (2013)Google Scholar
  2. 2.
    Doerr, M., Bekiari, C., LeBoeuf, P.: FRBRoo: a conceptual model for performing arts. In: CIDOC Annual Conference, pp. 6–18 (2008)Google Scholar
  3. 3.
    Choffé, P., Leresche, F.: DOREMUS: connecting sources, enriching catalogues and user experience. In: IFLA World Library and Information Congress (2016)Google Scholar
  4. 4.
    Lisena, P., Todorov, K., Cecconi, C., Leresche, F., Canno, I., Puyrenier, F., Voisin, M., Troncy, R.: Controlled vocabularies for music metadata. In: ISMIR (2018)Google Scholar
  5. 5.
    Bellahsene, Z., Emonet, V., Ngo, D., Todorov, K.: YAM++ Online: a web platform for ontology and thesaurus matching and mapping validation. In: Blomqvist, E., et al. (eds.) ESWC 2017. LNCS, vol. 10577, pp. 137–142. Springer, Cham (2017). Scholar
  6. 6.
    Lisena, P., Achichi, M., Fernández, E., Todorov, K., Troncy, R.: Exploring linked classical music catalogs with OVERTURE. In: ISWC (Posters & Demos) (2016)Google Scholar
  7. 7.
    Jentzsch, A., Isele, R., Bizer, C.: Silk-generating RDF links while publishing or consuming linked data. In: ISWC (2010)Google Scholar
  8. 8.
    Ngomo, A.N., Auer, S.: LIMES - a time-efficient approach for large-scale link discovery on the web of data. In: IJCAI, pp. 2312–2317 (2011)Google Scholar
  9. 9.
    Achichi, M., Ben Ellefi, M., Symeonidou, D., Todorov, K.: Automatic key selection for data linking. In: EKAW, pp. 3–18 (2016)Google Scholar
  10. 10.
    Achichi, M., Bellahsene, Z., Todorov, K.: Legato: Results for OAEI 2017. In: Ontology Matching (2017)Google Scholar
  11. 11.
    Rokach, L., Maimon, O.: Clustering methods. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 321–352. Springer, Boston (2005). Scholar
  12. 12.
    Symeonidou, D., Armant, V., Pernelle, N., Saïs, F.: SAKey: scalable almost key discovery in RDF data. In: Mika, P., Tudorache, T., Bernstein, A., Welty, C., Knoblock, C., Vrandečić, D., Groth, P., Noy, N., Janowicz, K., Goble, C. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 33–49. Springer, Cham (2014). Scholar
  13. 13.
    Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: SIGKDD (2016)Google Scholar
  14. 14.
    Lisena, P., Troncy, R.: Combining music specific embeddings for computing artist similarity. In: ISMIR, Late-Breaking Demo Track (2017)Google Scholar
  15. 15.
    Lisena, P., Canale, L., Ellena, F., Troncy, R.: CityMus: music recommendation when exploring a City. In: ISWC, Poster Track (2017)Google Scholar
  16. 16.
    Villazón-Terrazas, B., Vilches-Blázquez, L.M., Corcho, O., Gómez-Pérez, A.: Methodological Guidelines for Publishing Government Linked Data. In: Wood, D. (ed.) Linking Government Data, pp. 27–49. Springer, New York (2011). Scholar
  17. 17.
    Dijkshoorn, C., Jongma, L., Aroyo, L., van Ossenbruggen, J., Schreiber, G., ter Weele, W., Wielemaker, J.: The rijksmuseum collection as linked data. In: Semantic Web, pp. 1–10 (2014)Google Scholar
  18. 18.
    Candela, G., Escobar, P., Carrasco, R.C., Marco-Such, M.: Migration of a library catalogue into rda linked open data. Semantic Web, pp. 1–11 (2017)Google Scholar
  19. 19.
    Swartz, A.: Musicbrainz: a semantic web service. IEEE Intell. Syst. 17(1), 76–77 (2002)CrossRefGoogle Scholar
  20. 20.
    Jacobson, K., Dixon, S., Sandler, M.: Linkedbrainz: providing the musicbrainz next generation schema as linked data. In: Demo Session ISMIR (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Manel Achichi
    • 1
  • Pasquale Lisena
    • 2
  • Konstantin Todorov
    • 1
    Email author
  • Raphaël Troncy
    • 2
  • Jean Delahousse
    • 3
  1. 1.LIRMM, University of Montpellier, CNRSMontpellierFrance
  2. 2.EURECOMSophia AntipolisFrance
  3. 3.OUROUKParisFrance

Personalised recommendations