Misc,
An Ecosystem for Transparent Music Similarity in an Open World
(2010)
Abstract
There exist many methods for deriving music similarity associations and additional variations are likely to be seen in the future. In this work we introduce the Similarity Ontology for describing associations between items. Using a combination of RDF/OWL and N3, our ontology allows for transparency and provenance tracking in a distributed and open system. We describe a similarity ecosystem where agents assert and aggregate similarity statements on the Web of Data allowing a client application to make queries for recommendation, playlisting, or other tasks. In this ecosystem any number of similarity derivation methods can exist side-by-side, specifying similarity relationships as well as the processes used to derive these statements. The data consumer can then select which similarity statements to trust based on knowledge of the similarity derivation processes or a list of trusted assertion agents.
