Hello Cleveland!

Linked Data Publication of Live Music ArchivesSean Bechhofer*, Kevin Page+, David De Roure+

*School of Computer Science, University of Manchester+Oxford eResearch Centre, University of Oxford

@seanbechhofer

DMRN+7, QMUL, December 2012

The Proposition๏ Publication of structured metadata describing an audio

collection

๏ Links to external resources provide additional context and information

๏ Rich query to allow the extraction of “interesting” subcollections

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The Players• The Internet Archive Live Music Archive

✦ Community contributed live audio recordings

• Semantic Technologies✦ RDF, Ontologies, SPARQL and Linked Data

• Additional resources✦ Artist DBs, Geographical Information, Venue information, etc.

• Some ruby scripts.....

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The etree Collection• Internet Archive Live Music Archive• Community contributed live performance recordings

✦ “Legal bootlegs”

• Approx 4,000 artists,✦ 100,000 performances

• Why is it interesting?✦ Audio available in various formats

✤ mp3, ogg, shn, flac....✦ Multiple performances by artists✦ Cover versions

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Semantic Technologies• Semantic Technologies aim to provide structured, machine

readable representations of content✦ Unified frameworks for (meta)data

• RDF: Resource Description Framework✦ Triple based representation of information

• OWL/SKOS: Ontologies & Vocabularies for content description✦ Shared vocabularies plus definitional capabilities

• SPARQL✦ A query language for RDF data✦ A generic API

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Semantic TechnologiesRDF

• Triple Based Representation• Common Data Model• Identification via URIs • Easy Integration

✦ Graph Merging

• Query via SPARQL✦ A flexible, generic API

OWL/SKOS• Shared Vocabularies for

content description✦ Facilitating interoperation and

exchange✦ Everybody talks the same

language

• OWL allows for rich expressions and definitions

• SKOS supports simpler thesauri/controlled vocabularies

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Linked Data• A set of common principles for data publication

• Common infrastructure facilitates construction of applications.• Use of content negotiation to supply “appropriate”

representations

1. Use URIs for identification2. Use HTTP URIs (that will dereference)3. Return useful information when dereferenced 4. Include links in that information

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Linked Data Resources• MusicBrainz

✦ RDF conversions of MusicBrainz data

• Geonames✦ Information about locations

• DBpedia✦ Structured representation of Wikipedia content

• BBC✦ Programme information, artist information

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Data mangling• Download of etree metadata files• Simple data conversion

✦ XML to RDF✦ etree data model

• Alignments✦ String matching plus bespoke

methods for locations✦ Explicit capture of alignments

• Publication Infrastructure✦ fuseki server + pubby front end

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Modelling

Music OntologyEvent Ontology

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Data Alignment• MusicBrainz

✦ Artist alignment via simple name queries

• Geographical Locations✦ Query against Geonames✦ Query against last.fm✦ Combination of string matching and lat/long

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Layering• Alignments are captured in an additional layer of data on top of

the underlying source facts• Preserving original metadata

✦ Allows clients to make their own judgements✦ Preserves subjectivity

• Explicitly exposing the source of the mappings✦ Use of Provenance vocabularies

sameAs

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Modelling

Similarity Ontology

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Big Picture

Discussion• So far entirely metadata based

✦ No processing of underlying audio

• Alignment is a little messy✦ But has to be automated

• Dataset itself is an interesting artefact✦ Contrasts with some other LD activities.

• Is this actually useful?

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Do artists really get a better reception when they play in their home town?

The Future• Better alignment

✦ Beyond simple string queries

• More alignment✦ Adding in, e.g. MusicBrainz track/work resources✦ Other collections?✦ Modelling questions

• Characterising Alignments• Audio Fingerprinting

✦ Identifying further track level matches

• Crowdsourcing corrections• Extracting subcollections

✦ What would you want?? 30

Thanks! You’ve been a great audience!

http://etree.linkedmusic.org31