Abstract
A variety of digital data sources—including institutional and formal digital libraries, crowd-sourced community resources, and data feeds provided by media organisations such as the BBC—expose information of musicological interest, describing works, composers, performers, and wider historical and cultural contexts. Aggregated access across such datasets is desirable as these sources provide complementary information on shared real-world entities. Where datasets do not share identifiers, an alignment process is required, but this process is fraught with ambiguity and difficult to automate, whereas manual alignment may be time-consuming and error-prone. We address this problem through the application of a Linked Data model and framework to assist domain experts in this process. Candidate alignment suggestions are generated automatically based on textual and on contextual similarity. The latter is determined according to user-configurable weighted graph traversals. Match decisions confirming or disputing the candidate suggestions are obtained in conjunction with user insight and expertise. These decisions are integrated into the knowledge base, enabling further iterative alignment, and simplifying the creation of unified viewing interfaces. Provenance of the musicologist’s judgement is captured and published, supporting scholarly discourse and counter-proposals. We present our implementation and evaluation of this framework, conducting a user study with eight musicologists. We further demonstrate the value of our approach through a case study providing aligned access to catalogue metadata and digitised score images from the British Library and other sources, and broadcast data from the BBC Radio 3 Early Music Show.
Highlights
The reconciliation of corpora providing access to historical catalogue data in a digital libraries context is made difficult by a range of challenges from ambiguities concerning the names of individuals to disputed or erroneous attribution (e.g. [1])
We present a Semantic Alignment and Linking Tool (SALT) that implements the model and design introduced in the previous section
We present the Semantic Linking of BBC Radio (SLoBR) demonstrator, a web application inspired by the look and feel of the existing Early Music Show (EMS) web resource while providing access to biographical information, bibliographical catalogue data, and digitised musical score available via alignment to the SLICKMEM dataset, and via further external datasets made available by this alignment
Summary
The reconciliation of corpora providing access to historical catalogue data in a digital libraries context is made difficult by a range of challenges from ambiguities concerning the names of individuals to disputed or erroneous attribution (e.g. [1]). Relevant sources include digital libraries of institutions such as the British Library, formal digital library resources provided by organisations such as the OCLC1 (e.g. VIAF2), data feeds provided by commercial and media industry institutions such as the BBC (the UK’s national public service broadcaster), and community resources such as MusicBrainz.. Relevant sources include digital libraries of institutions such as the British Library, formal digital library resources provided by organisations such as the OCLC1 (e.g. VIAF2), data feeds provided by commercial and media industry institutions such as the BBC (the UK’s national public service broadcaster), and community resources such as MusicBrainz.3 Such datasets provide complementary information concerning the same historical entities (e.g. composers, works), but the corresponding records may not share identifiers across the datasets. In: Proceedings of the 12th International Society for Music Information Retrieval Conference, pp. In: Proceedings of the 24th International Conference on World Wide
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