Predicting High-level Music Semantics Using Social Tags via Ontology-based Reasoning

Predicting High-level Music Semantics Using Social Tags via Ontology-based Reasoning
Jun Wang, Xiaoou Chen, Yajie Hu and Tao Feng

ABSTRACT – High-level semantics such as “mood” and “usage” are very useful in music retrieval and recommendation but they are normally hard to acquire. Can we predict them from a cloud of social tags? We propose a semantic iden- tification and reasoning method: Given a music taxonomy system, we map it to an ontology’s terminology, map its finite set of terms to the ontology’s assertional axioms, and then map tags to the closest conceptual level of the referenced terms in WordNet to enrich the knowledge base, then we predict richer high-level semantic informa- tion with a set of reasoning rules. We find this method predicts mood annotations for music with higher accuracy, as well as giving richer semantic association information, than alternative SVM-based methods do.

In this paper, the authors use word-net to map social tags to a professional taxonomy and then use these for traditional tagging tasks such as classification and mood identification.

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