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The Timeline Ontology is centered around the notion of timeline, seen here as a way to identify a temporal backbone. A timeline may support a signal, a video, a score, a work, etc.
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The Music Ontology Specification provides main concepts and properties fo describing music (i.e. artists, albums, tracks, but also performances, arrangements, etc.) on the Semantic Web.
Post-receive hook for the Music Ontology specification
The XSPF RDFizer consists of a XSPF to Music Ontology RDF XSL stylesheet.
The Multitrack Ontology is an ontology for describing concepts in multitrack media production.
The DBTune Last.fm artist similarity RDF service provides an RDF representation of music artists and their similar artists based on the Last.fm API.
mopy is the Music Ontology Python library, designed to provide easy to use python bindings for Music Ontology terms for the creation and manipulation of Music Ontology data.
The Sonic Annotator Web Application analyses uploaded audio files using Vamp feature extractor plugins.
The DBTune MySpace Wrapper Service wraps content of MySpace sites to knowledge representations that are powered by several Semantic Web ontologies.
This is a D2RQ mapping of a pre-NGS MusicBrainz relational database schema to knowledge representations that are powered by several Semantic Web ontologies, especially the Music Ontology.
The DBTune AudioScrobbler RDF Service provides a Last.fm API to RDF converter.
A piece of software that configures a UI to display Jamendo-powered Linked Data.
This is a python library version of the GNAT software.
The GNAT audio items linker enables to derive statements linking a set of audio items to a set of dereferencable identifiers, and to store them in a RDF store.
GNARQL creates an aggregation of structured web data focused on a user's audio collection. It exposes this aggregation through a SPARQL enpoint.
The Event Notes Ontology is a small ontology to take quick notes during events, such as conferences etc.
The DBTune Echo Nest artist similarity service provides an RDF wrapper around the Echo Nest API for artist similarity.
DBTune Echo Nest Analyze API RDFizer utilises a simple XSL for transforming the results of the Echo Nest Analyze API to Music Ontology RDF.
DBTune hosts a number of servers, providing access to music-related structured data, in a Linked Data fashion.
The Tonality Ontology is aimed to give high-level and low-level descriptors for tonal content in RDF.
The Symbolic Notation Ontology is designed for describing (western) musical notations.
The temperament ontology aims to describe instrument tuning systems and their particularities. It may also be used to characterise a (potentially unknown) temperament that was used when tuning an instrument for a particular performance or recording.
The Keys Ontology is designed for describing keys in musical pieces.
The Event Ontology deals with the notion of reified events. It defines one main Event concept. An event may have a location, a time, active agents, factors and products.
The Chord Ontology is an ontology for describing chords in musical pieces.
This ontology express the most common things you may extract automatically from a musical signal, by sub-classing some of the terms defined in the Music Ontology.