A distributed music information system
Here, you can find information about my PhD thesis, defended on the 22nd of January, 2009, and entitled A Distributed Music Information System. My PhD supervisor was Mark Sandler from the Centre for Digital Music in Queen Mary, University of London. My examiners were David de Roure from the University of Southampton and Nicolas Gold from King's College.
Abstract:
Information management is an important part of music technologies today, covering the man- agement of public and personal collections, the construction of large editorial databases and the storage of music analysis results. The information management solutions that have emerged for these use-cases are still isolated from each other. The information one of these solutions manages does not benefit from the information another holds.
In this thesis, we develop a distributed music information system that aims at gathering music- related information held by multiple databases or applications. To this end, we use Semantic Web technologies to create a unified information environment. Web identifiers correspond to any items in the music domain: performance, artist, musical work, etc. These web identifiers have structured representations permitting sophisticated reuse by applications, and these representations can quote other web identifiers leading to more information.
We develop a formal ontology for the music domain. This ontology allows us to publish and interlink a wide range of structured music-related data on the Web. We develop an ontology evaluation methodology and use it to evaluate our music ontology. We develop a knowledge representation framework for combining structured web data and analysis tools to derive more information. We apply these different technologies to publish a large amount of pre-existing music-related datasets on the Web. We develop an algorithm to automatically relate such datasets among each other. We create automated music-related Semantic Web agents, able to aggregate musical resources, structured web data and music processing tools to derive and publish new information. Finally, we describe three of our applications using this distributed information environment. These applications deal with personal collection management, enhanced access to large audio streams available on the Web and music recommendation.
