Kurt Jacobson

An Overview of Semantic Web Activities in the OMRAS2 Project

Abstract The use of cultural information is becoming increasingly important in music information research, especially in music retrieval and recommendation. While this information is widely available on the Web, it is most commonly published using proprietary Web Application Programming Interfaces (APIs). The Linked Data community is aiming at resolving the incompatibilities between these diverse data sources by building a Web of data using Semantic Web technologies. The OMRAS2 project has made several important contributions to this by developing an ontological framework and numerous software tools, as well as publishing music related data on the Semantic Web. These data and tools have found their use even beyond their originally intended scope. In this paper, we first provide a broad overview of the Semantic Web technologies underlying this work. We describe the Music Ontology, an open-ended framework for communicating musical information on the Web, and show how this framework can be extended to describe specific sub-domains such as music similarity, content-based audio features, musicological data and studio production. We describe several data-sets that have been published and data sources that have been adapted using this framework. Finally, we provide application examples ranging from software libraries to end user Web applications.

Analysis and Exploitation of Musician Social Networks for Recommendation and Discovery

This paper presents an extensive analysis of a sample of a social network of musicians. The network sample is first analyzed using standard complex network techniques to verify that it has similar properties to other web-derived complex networks. Content-based pairwise dissimilarity values between the musical data associated with the network sample are computed, and the relationship between those content-based distances and distances from network theory explored. Following this exploration, hybrid graphs and distance measures are constructed, and used to examine the community structure of the artist network. Finally, results of these investigations are shown to be mostly orthogonal between these distance spaces. These results are considered with a focus recommendation and discovery applications employing these hybrid measures as their basis.

Musically Meaningful or Just Noise? An Analysis of On-line Artist Networks

A sample of the Myspace social network is examined. Using methods from complex network theory, we show empirically that the structure of the Myspace artist network is related to the concept of musical genre. A modified assortativity coefficient calculation shows that artists preferentially form network connections with other artists of the same genre. We also show there is a clear trend relating the geodesic distance between artists and genre label associations - that is artists with the same genre associations tend to be closer in the network. These findings motivate the use of on-line social networks as data resources for musicology and music information retrieval.