Knowledge Extraction from Audio Content Service Providers’ API Descriptions

  • Damir JuricEmail author
  • György Fazekas
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 672)


Creating an ecosystem that will tie together the content, technologies and tools in the field of digital music and audio is possible if all the entities of the ecosystem share the same vocabulary and high quality metadata. Creation of such metadata will allow the creative industries to retrieve and reuse the content of Creative Commons audio in innovative new ways. In this paper we present a highly automated method capable of exploiting already existing API (Application Programming Interface) descriptions about audio content and turning it into a knowledge base that can be used as a building block for ontologies describing audio related entities and services.


Metadata Audio content Ontologies Natural language processing Knowledge extraction 


  1. 1.
    Aslam, N., Ullah, I., Rohullah, B.S., Akram, T., Shabir, M.: Tracking the progression of multimedia semantics: from text based retrieval to semantic based retrieval. World Appl. Sci. J. 20(4), 549–553 (2012)Google Scholar
  2. 2.
    Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  3. 3.
    Krotzsch, M., Vrandecic, D., Volkel, M., Haller, H., Studer, R.: Semantic Wikipedia. J. Web Semant. 5(4), 251–261 (2007)CrossRefGoogle Scholar
  4. 4.
    Zhou, L.: Ontology learning: state of the art and open issues. Inf. Technol. Manag. 8(3), 241–252 (2007)CrossRefGoogle Scholar
  5. 5.
    Wisniewski, M.: Metamodel of ontology learning from text. In: Badr, Y., Chbeir, R., Abraham, A., Hassanien, A.-E. (eds.) Emergent Web Intelligence: Advanced Semantic Technologies. Advanced Information and Knowledge Processing, pp. 245–276. Springer, London (2010)CrossRefGoogle Scholar
  6. 6.
    Raimond, Y., Abdallah, S., Sandler, M., Giasson, F.: The music ontology. In: International Society for Music Information Retrieval Conference, pp. 417–422 (2007)Google Scholar
  7. 7.
    Fazekas, G., Sandler, M.B.: The studio ontology framework. In: 12th International Society for Music Information Retrieval Conference (2011)Google Scholar
  8. 8.
    Saur., K.G.: Functional requirements for bibliographic records: final report, vol. 19, 136 p. (1998). UBCIM Publications, ISBN: 978-3-598-11382-6Google Scholar
  9. 9.
    Allik, A., Fazekas, G., Sandler, M.B.: An ontology for audio features. In: 17th International Society for Music Information Retrieval Conference (2016)Google Scholar
  10. 10.
    De Nicola, A., Missikoff, M., Navigli, R.: A software engineering approach to ontology building. Inf. Syst. 34(2), 258–275 (2009)CrossRefGoogle Scholar
  11. 11.
    Angeli, G., Premkumar, M.J., Manning., C.D.: Leveraging linguistic structure for open domain information extraction. In: Proceedings of the Association of Computational Linguistics (ACL) (2015)Google Scholar
  12. 12.
    de Marneffe, C.-M., MacCartney, B., Manning, C.D.: Generating typed dependency parses from phrase structure parses. In: Proceedings of LREC 2006, pp. 449–454 (2006)Google Scholar
  13. 13.
    Palmer, M., Gildea, D., Kingsbury, P.: The Proposition Bank: an annotated corpus of semantic roles. Computat. Linguist. 31(1), 71–106 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Queen Mary University of LondonLondonUK

Personalised recommendations