Skip to main content
SpringerLink
Log in

MusicWeb: Music Discovery with Open Linked Semantic Metadata

  • Conference paper
  • First Online:
Metadata and Semantics Research (MTSR 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 672))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://musicbrainz.org.

  2. 2.

    http://dbpedia.org.

  3. 3.

    http://sameas.org.

  4. 4.

    http://last.fm.

  5. 5.

    http://www.bbc.co.uk/music/artists/.

  6. 6.

    http://dev.mendeley.com/.

  7. 7.

    http://dev.elsevier.com/.

  8. 8.

    AlchemyAPI is used under license from IBM Watson.

  9. 9.

    https://acousticbrainz.org/.

References

  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. 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)

    Chapter  Google Scholar 

  3. Celma, Ò.: Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space. Springer, Heidelberg (2010)

    Book  Google Scholar 

  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. Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(9), 41–46 (2006)

    Article  Google Scholar 

  6. Raimond, Y., Abdallah, S.A., Sandler, M.B., Giasson, F.: The music ontology. In: ISMIR, pp. 417–422. Citeseer (2007)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Queen Mary University, London, UK

    Mariano Mora-Mcginity, Alo Allik, György Fazekas & Mark Sandler

Authors
  1. Mariano Mora-Mcginity
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alo Allik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. György Fazekas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mark Sandler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mariano Mora-Mcginity .

Editor information

Editors and Affiliations

  1. Alexander Technological Educational Institute of Thessaloniki, Thessaloniki, Greece

    Emmanouel Garoufallou

  2. Alexander Technological Educational Inst , Rome, Italy

    Imma Subirats Coll

  3. Sapienza University of Rome , Rome, Italy

    Armando Stellato

  4. Drexel University, Philadelphia, Pennsylvania, USA

    Jane Greenberg

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Mora-Mcginity, M., Allik, A., Fazekas, G., Sandler, M. (2016). MusicWeb: Music Discovery with Open Linked Semantic Metadata. In: Garoufallou, E., Subirats Coll, I., Stellato, A., Greenberg, J. (eds) Metadata and Semantics Research. MTSR 2016. Communications in Computer and Information Science, vol 672. Springer, Cham. https://doi.org/10.1007/978-3-319-49157-8_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-49157-8_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49156-1

  • Online ISBN: 978-3-319-49157-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation