Expert-guided semantic linking of music-library metadata for study and reuse

Abstract

The process of aligning datasets that lack mutually-shared identifiers is fraught with ambiguity and difficult to automate. Manual performance of such a process may be time-consuming and error-prone. We present the Semantic Alignment and Linking Tool (SALT) that addresses this problem by applying semantic technologies and Linked Data approaches in order to produce candidate alignment suggestions that may be confirmed or disputed by a user with domain expertise. These decisions are integrated back into the knowledge base and are available for further iterative comparison by the user; the complete RDF graph is published and can be queried through the same SPARQL endpoint that also underlies the SALT user interface. Provenance of the musicologist's judgement is captured and added to the descriptive graph, supporting further discourse and counter-proposals. We report on a use case and perform an evaluation of this tool within a musicological context, joining metadata from the British Library and other sources with programme data from BBC Radio 3 in a project focusing on early music.