In making music metadata available on the semantic web, we are addressing the needs of two primary types of user in the HE sector: (i) those working in the Music Informatics (or Music Information Retrieval) community, primarily in computing and engineering departments; and (ii) musicologists and musicians working in music departments. This is in addition to the (sizable) international MusicBrainz user community, who also stand to benefit from the linking of their data to other semantic web resources, as well as software developers working with on-line music services, and their users, who will indirectly benefit from this project.
The need that is central to this project is that of being able to identify musical entities (e.g. artists and recordings) unambiguously. Until the last decade, this was rarely an important issue. Data sets were small and their owners were aware of their content, often at an expert level. But as the size of music collections increases, and data is processed automatically rather than manually, it becomes essential to manage the metadata about the collections in a principled way. Linked data provides a means of joining different information sources, enabling the added value of "unexpected re-use of information" (Berners Lee, 2006) to be realised. The utility of linked data is however limited by the extent of the interlinking that exists between related data sets, and the current multiplicity of URIs for artists, albums and tracks is a potential hindrance to the goals of linked data. Since MusicBrainz provides the most extensive open metadatabase for musical recordings and is already a de facto standard in its field, it would be advantageous to establish it as a standard for Linked Data as well. For this to take place, the database must be exposed on the semantic web with infrastructure to ensure that updates are automatic or propagated in a timely and sustainable manner. The current RDF translation of the MusicBrainz database contains only basic metadata and does not map any of MusicBrainz' Advanced Relationship data. This richer data is being refactored according to the soon-to-be-released NGS, which, if exposed as linked data, will allow users to make more expressive queries and receive more useful responses. For example, a musicologist or music student searching for recordings of a particular orchestra or composer immediately faces a problem which renders the semantic web useless to them: the current version of the MusicBrainz database uses the categories artist, album and track, and for classical music, users might have entered the performer, composer, or a mixture of the two in the artist field. This problem is addressed by the NGS. Likewise a music student might want to find tracks featuring a particular saxophonist or lyricist, or find out if there are any live recordings of a particular piece for which they have only a studio version. Further, an MIR researcher developing an algorithm that classifies music by genre using audio features might want to test whether the recording engineer or studio have an effect on classification results. For the wider semantic web community, the exposure of MusicBrainz' NGS will provide a hub for talking about music and lay the groundwork for advanced semantic web applications. The longer term impact of the project, i.e. beyond the end of the project, will be seen in terms of uptake in the user communities, for example by the tools and services which are enriched or enabled by the availability of the data on the semantic web. In particular, we envisage that music students will have tools for identifying and researching recordings, and that musicologists and MIR specialists will be able to perform new types of research, answering new research questions involving orders of magnitude more data than they would have previously been able to consider. The host institution has a large research group working at the interface of music and technology. In particular, we are very active in the Music Information Retrieval (MIR) community, working on the annotation and navigation of large music collections using semantic web technologies (see for example www.omras2.org). Exposing and linking the MusicBrainz database will be beneficial for present and future projects.