Advertisement

MusicWeb: Music Discovery with Open Linked Semantic Metadata

  • Mariano Mora-McginityEmail author
  • Alo Allik
  • György Fazekas
  • Mark Sandler
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 672)

Abstract

This paper presents MusicWeb, a novel platform for music discovery by linking music artists within a web-based application. MusicWeb provides a browsing experience using connections that are either extra-musical or tangential to music, such as the artists’ political affiliation or social influence, or intra-musical, such as the artists’ main instrument or most favoured musical key. The platform integrates open linked semantic metadata from various Semantic Web, music recommendation and social media data sources. Artists are linked by various commonalities such as style, geographical location, instrumentation, record label as well as more obscure categories, for instance, artists who have received the same award, have shared the same fate, or belonged to the same organisation. These connections are further enhanced by thematic analysis of journal articles, blog posts and content-based similarity measures focussing on high level musical categories.

Keywords

Semantic Web Linked open data Music metadata Semantic audio analysis Music information retrieval 

References

  1. 1.
    Song, Y., Dixon, S., Pearce, M.: A survey of music recommendation systems and future perspectives. In: 9th International Symposium on Computer Music Modeling and Retrieval (2012)Google Scholar
  2. 2.
    Sneha, S., Jayalakshmi, D.S., Shruthi, J., Shetty, U.R.: Recommending music by combining content-based and collaborative filtering with user preferences. In: Sridhar, V., Sheshadri, H.S., Padma, M.C. (eds.) ICERECT 2012. LNCS, vol. 248, pp. 507–515. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  3. 3.
    Celma, Ò.: Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Fabian, M.S., Gjergji, K., Gerhard, W.: Yago: a core of semantic knowledge unifying wordnet and wikipedia. In: 16th International World Wide Web Conference, WWW, pp. 697–706 (2007)Google Scholar
  5. 5.
    Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(9), 41–46 (2006)CrossRefGoogle Scholar
  6. 6.
    Raimond, Y., Abdallah, S.A., Sandler, M.B., Giasson, F.: The music ontology. In: ISMIR, pp. 417–422. Citeseer (2007)Google Scholar
  7. 7.
    Rodríguez-García, M., Colombo-Mendoza, L.O., Valencia-García, R., Lopez-Lorca, A.A., Beydoun, G.: Ontology-based music recommender system. In: Omatu, S., Malluhi, Q.M., Gonzalez, S.R., Bocewicz, G., Bucciarelli, E., Giulioni, G., Iqba, F. (eds.) Distributed Computing and Artificial Intelligence, 12th International Conference, vol. 373, pp. 39–46. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  8. 8.
    Casey, M.A., Veltkamp, R., Goto, M., Leman, M., Rhodes, C., Slaney, M.: Content-based music information retrieval: current directions and future challenges. IEEE Proc. 96(4), 668–696 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Mariano Mora-Mcginity
    • 1
    Email author
  • Alo Allik
    • 1
  • György Fazekas
    • 1
  • Mark Sandler
    • 1
  1. 1.Queen Mary UniversityLondonUK

Personalised recommendations