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A variety of digital data sources---including institutional and formal digital libraries, crowd-sourced community resources, and data feeds provided by media organizations 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

On providing semantic alignment and unified access to music library metadata