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Building listening experience Linked Data through crowd-sourcing and reuse of library data

Brown, Simon; Adamou, Alessandro; Barlow, Helen and d'Aquin, Mathieu (2014). Building listening experience Linked Data through crowd-sourcing and reuse of library data. In: Proceedings of the 1st International Workshop on Digital Libraries for Musicology - DLfM '14, DLfM '14, ACM, pp. 1–8.

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The Listening Experience Database (LED) is a project that gathers documented evidence of listening to music across cultural and historical contexts. Its underlying information system relies on the principles and practices of Linked Data, including a knowledge base that is itself a linked dataset structured according to common vocabularies for media and bibliographies such as Bibo and the Music Ontology. The data management workflow fully supports crowd-sourced input and incorporates data reuse from various sources right from the point of data collection, including the British National Bibliography dataset. The LED system gives contributors and moderators the tools to enhance these data by modelling divisions of bibliographc entries, providing information beyond the BNB schema, or aligning with other datasets by simple data reconciliation procedures. Vocabularies for music genre and instrument classes are also crowd-sourced on top of a baseline taxonomy from the DBpedia dataset. The Web frontend uses the Drupal content management system to encapsulate listening experiences into digital objects that incorporate textual information with references to literary, musical and other classes of related entities.

Item Type: Conference or Workshop Item
Copyright Holders: 2014 ACM
ISBN: 1-4503-3002-9, 978-1-4503-3002-2
Project Funding Details:
Funded Project NameProject IDFunding Body
The Listening Experience Database (A-11-031-DR)AH/J013986/1AHRC (Arts & Humanities Research Council)
Keywords: musicology; linked data; digital libraries; listening experience; crowd-sourcing
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Arts and Social Sciences (FASS) > Arts and Cultures
Faculty of Arts and Social Sciences (FASS)
Research Group: Centre for Research in Computing (CRC)
Item ID: 42048
Depositing User: Alessandro Adamou
Date Deposited: 17 Feb 2015 14:47
Last Modified: 05 Oct 2016 12:28
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