Modeling, Exploring and Recommending Music in Its Complexity

Knowledge models that are currently in-use for describing music metadata are insufficient to express the wealth of complex information about creative works, performances, publications, authors and performers. In this thesis, we aim to propose a method for structuring the music information coming from heterogeneous librarian repositories. In particular, we research and design an appropriate music ontology based on existing models and controlled vocabularies and we implement tools for converting and visualizing the metadata. Moreover, we research how this data can be consumed by end-users, through the development of a web application for exploring the data. We ultimately aim to develop a recommendation system that takes advantage of the richness of the data.

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