Harvesting and Structuring Social Data in Music Information Retrieval

  • Sergio Oramas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8465)


An exponentially growing amount of music and sound resources are being shared by communities of users on the Internet. Social media content can be found with different levels of structuring, and the contributing users might be experts or non-experts of the domain. Harvesting and structuring this information semantically would be very useful in context-aware Music Information Retrieval (MIR). Until now, scant research in this field has taken advantage of the use of formal knowledge representations in the process of structuring information. We propose a methodology that combines Social Media Mining, Knowledge Extraction and Natural Language Processing techniques, to extract meaningful context information from social data. By using the extracted information we aim to improve retrieval, discovery and annotation of music and sound resources. We define three different scenarios to test and develop our methodology.


#eswcphd2014Oramas social media mining knowledge extraction natural language processing information retrieval music 


  1. 1.
    Sordo, M.: Semantic Annotation of Music Collections: A Computational Approach. PhD Thesis (2011)Google Scholar
  2. 2.
    Schedl, M., Schnitzer, D.: Location-Aware Music Artist Recommendation. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MMM 2014, Part II. LNCS, vol. 8326, pp. 205–213. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  3. 3.
    Schedl, M.: On the Use of the Web and Social Media in Multimodal Music Information Retrieval. Postdoctoral Thesis (Habilitation) (2013)Google Scholar
  4. 4.
    Serra, X.: Exploiting Domain Knowledge in Music Information Research. In: Stockholm Music Acoustics Conference 2013 and Sound and Music Computing Conference (2013)Google Scholar
  5. 5.
    Mika, P.: Ontologies are us: A unified model of social networks and semantics. Web Semantics: Science, Services and Agents on the World Wide Web 5, 5–15 (2007)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Gangemi, A.: A Comparison of Knowledge Extraction Tools for the Semantic Web. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 351–366. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Alani, H., Millard, D., Weal, M., Hall, W., Lewis, P., Shadbolt, N.: Automatic ontology-based knowledge extraction from Web documents. IEEE Intelligent Systems 18, 14–21 (2003)CrossRefGoogle Scholar
  8. 8.
    Font, F., Roma, G., Herrera, P., Serra, X.: Characterization of the Freesound online community. In: 2012 3rd International Workshop on Cognitive Information Processing (CIP), pp. 1–6 (2012)Google Scholar
  9. 9.
    Echarte, F., Astrain, J.J., Córdoba, A., Villadangos, J.: Ontology of Folksonomy: A New Modeling Method. In: SAAKM 2007, p. 8 (2007)Google Scholar
  10. 10.
    Sharif, A.: Combining ontology and folksonomy: An Integrated Approach to Knowledge Representation. In: Emerging Trends in Technology Libraries Between Web 20 Semantic Web and Search Technology, pp. 1–13 (2007)Google Scholar
  11. 11.
    Lohmann, S., Díaz, P., Aedo, I.: MUTO: the modular unified tagging ontology. In: Proceedings of the 7th International Conference on Semantic Systems - I-Semantics 2011, pp. 95–104 (2011)Google Scholar
  12. 12.
    Angeletou, S., Sabou, M., Specia, L., Motta, E.: Bridging the Gap Between Folksonomies and the Semantic Web: An Experience Report. In: European Semantic Web Conference, pp. 30–43 (2007)Google Scholar
  13. 13.
    Cohen, W.W., Fan, W.: Web-collaborative filtering: recommending music by crawling the Web. Computer Networks 33, 685–698 (2000)CrossRefGoogle Scholar
  14. 14.
    Ellis, D.P.W., Ellis, D.P., Whitman, B., Whitman, B., Berenzweig, A., Berenzweig, A., Lawrence, S., Lawrence, S.: The quest for ground truth in musical artist similarity. In: Proc. International Symposium on Music Information Retrieval (ISMIR 2002), pp. 170–177 (2002)Google Scholar
  15. 15.
    Whitman, B., Lawrence, S.: Inferring descriptions and similarity for music from community metadata. In: Proceedings of the 2002 International Computer Music Conference, pp. 591–598 (2002)Google Scholar
  16. 16.
    Schedl, M., Knees, P., Widmer, G.: A Web-Based Approach to Assessing Artist Similarity using Co-Occurrences. In: Proceedings of the 4th International Workshop on Content-Based Multimedia Indexing, CBMI 2005 (2005)Google Scholar
  17. 17.
    Schedl, M., Hauger, D., Urbano, J.: Harvesting microblogs for contextual music similarity estimation: a co-occurrence-based framework. Multimedia Systems (2013)Google Scholar
  18. 18.
    Sordo, M., Serrà, J., Serra, X.: A Method for Extracting Semantic Information from on-line Art Music Discussion Forums. In: 2nd CompMusic Workshop, pp. 55–60 (2012)Google Scholar
  19. 19.
    Schedl, M., Widmer, G., Knees, P., Pohle, T.: A music information system automatically generated via Web content mining techniques. Information Processing and Management: an International Journal 47 (2011)Google Scholar
  20. 20.
    Sordo, M., Gouyon, F., Sarmento, L., Celma, O., Serra, X.: Inferring Semantic Facets of a Music Folksonomy with Wikipedia. Journal of New Music Research 42, 346–363 (2013)CrossRefGoogle Scholar
  21. 21.
    Knees, P., Schedl, M.: Towards Semantic Music Information Extraction from the Web Using Rule Patterns and Supervised Learning. In: Colocated with ACM RecSys 2011, Chicago, IL, USA, October 23, p. 18 (2011)Google Scholar
  22. 22.
    Kolozali, S., Barthet, M., Fazekas, G., Sandler, M.B.: Automatic Ontology Generation for Musical Instruments Based on Audio Analysis. IEEE Transactions on Audio, Speech, and Language Processing 21, 2207–2220 (2013)CrossRefGoogle Scholar
  23. 23.
    Font, F., Serra, X., Gorup, M.T., Fabra, U.P.: Folksonomy-based tag recommendation for online audio clip sharing. In: Proceedings of 13th International Society for Music Information Retrieval Conference, Oporto (2012)Google Scholar
  24. 24.
    Font, F., Serra, X.: Analysis of the folksonomy of freesound. In: Proceedings of the 2nd CompMusic Workshop, pp. 48–54 (2012)Google Scholar
  25. 25.
    Font, F., Serr, J.: Audio clip classification using social tags and the effect of tag expansion. In: AES 53rd International Conference on Semantic Audio, pp. 1–9 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sergio Oramas
    • 1
  1. 1.Music Technology GroupUniversitat Pompeu FabraBarcelonaSpain

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