Characterizing the Landscape of Musical Data on the Web: state of the art and challenges

Musical data can be analysed, combined, transformed and exploited for diverse purposes. However, despite the proliferation of digital libraries and repositories for music, infrastructures and tools, such uses of musical data remain scarce. As an initial step to help fill this gap, we present a survey of the landscape of musical data on the Web, available as a Linked Open Dataset: the musoW dataset of catalogued musical resources. We present the dataset and the methodology and criteria for its creation and assessment. We map the identified dimensions and parameters to existing Linked Data vocabularies, present insights gained from SPARQL queries, and identify significant relations between resource features. We present a thematic analysis of the original research questions associated with surveyed resources and identify the extent to which the collected resources are Linked Data-ready.

[1]  J. Stephen Downie,et al.  Survey Of Music Information Needs, Uses, And Seeking Behaviours: Preliminary Findings , 2004, ISMIR.

[2]  Tim Berners-Lee,et al.  Linked Data - The Story So Far , 2009, Int. J. Semantic Web Inf. Syst..

[3]  Colin Raffel,et al.  Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching , 2016 .

[4]  Enrico Motta,et al.  Bottom-Up Ontology Construction with Contento , 2015, International Semantic Web Conference.

[5]  Frank van Harmelen,et al.  Semantic technologies for historical research: A survey , 2014, Semantic Web.

[6]  Mathieu d'Aquin,et al.  Where to publish and find ontologies? A survey of ontology libraries , 2012, J. Web Semant..

[7]  Christoph Lange,et al.  Ontologies and languages for representing mathematical knowledge on the Semantic Web , 2013, Semantic Web.

[8]  V. Braun,et al.  Using thematic analysis in psychology , 2006 .

[9]  Zaïd Harchaoui,et al.  Learning Features of Music from Scratch , 2016, ICLR.

[10]  H. Honing On the Growing Role of Observation, Formalization and Experimental Method in Musicology , 2006 .

[11]  Curtis Roads,et al.  Research in music and artificial intelligence , 1985, CSUR.

[12]  Yolanda Gil Workflow Composition: Semantic Representations for Flexible Automation , 2007, Workflows for e-Science, Scientific Workflows for Grids.

[13]  Thierry Bertin-Mahieux,et al.  The Million Song Dataset , 2011, ISMIR.

[14]  Tim Crawford,et al.  Exploring information retrieval, semantic technologies and workflows for music scholarship: the Transforming Musicology project , 2015 .

[15]  Markus Schedl,et al.  Music Information Retrieval: Recent Developments and Applications , 2014, Found. Trends Inf. Retr..

[16]  Xavier Serra,et al.  Evaluation in Music Information Retrieval , 2013, Journal of Intelligent Information Systems.

[17]  J. Stephen Downie,et al.  Capturing the workflows of music information retrieval for repeatability and reuse , 2013, Journal of Intelligent Information Systems.

[18]  Remco C. Veltkamp,et al.  A Survey of Music Information Retrieval Systems , 2005, ISMIR.

[19]  J. Reiss,et al.  Benchmarking Music Information Retrieval Systems , 2002 .

[20]  Cameron D. Palmer,et al.  Association Testing of Previously Reported Variants in a Large Case-Control Meta-analysis of Diabetic Nephropathy , 2011, Diabetes.