Using Pivots to Explore Heterogeneous CollectionsA Case Study in Musicology

Daniel Alexander Smith8 December 2009

musicSpace

http://mspace.fm/projects/musicspace

• IAM Group, School of Electronics and Computer Science

• Music, School of Humanities

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Outline• How musicologists use data

• Limitations of existing approaches

• Our data extraction and integration methodology

• Interface walkthrough

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musicSpace Tasks• Triage data partners sources

• Extract information

• Map data sources to schemas/ontologies

• Produce interface over aggregated data

• Customise interface based on feedback

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Data in Musicology

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Musicologists consult many data sources

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. . . but what if they could use just one?

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Intractable research questions• Which scribes have created manuscripts of a

composer’s works, and which other composers’ works have they inscribed?

• Which poets have had their poems set to music by Schubert, which of these musical settings were only published posthumously, and where can I find recordings of them?

• Which electroacoustic works were published within five years of their premier?

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Why they are intractable (1)• Need to consult several sources

• Metadata from one source cannot be used to guide searches of another source

• Solution: Integrate sources

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Why they are intractable (2)• They are multi-part queries, and need to be broken

down with results collated manually

• Requires pen and paper!

• Solution: Optimally interactive UI

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Why they are intractable (3)• Insufficient granualrity of metadata and/or search

option

• Solution: Increase granularity

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Metadata Extraction

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Previous work• Comb-e-chem modelled Chemistry data

• We use similar approach

• Translated this work to the arts

• Musicology modelled using Semantic Web technologies

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Musicology Data Sources• Disparate data

• How to pull them together and view on demand

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Public British Library Music Collections British Library Sound Archive Cecilia Copac RISM (UK and Ireland)

Commercial

Grove Music Online Naxos Music LibraryRILM

Future? Alexander Street Press MusicOnlineCHARM‘Personal’ datasets

musicSpace Data Partners

Data and Info Management problems• Sources allow searching, but not over everything

• Data export (MARC typically) shows extra fields, e.g. characters in opera, document types hidden amongst metadata

• Sometimes viewable on original site, but not searchable

• Offering extracted metadata already a benefit with one source

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Grove Extraction Example• More complicated, as Grove is a full text

encyclopaedia

• Some digitisation via Grove Music Online

• Weak semantic metadata extraction

• Thus we performed some data entry

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Grove Works Lists Source Data

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Works List Metadata Tool

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Data Integration

Integration• Domain Expert + Technologist partnership

• This will be case for some time now

• Technology to best automate tasks to make domain expert’s job less onerous

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Metadata mapping• Domain experts devise single schema

• Provide mappings of fields in a particular data source to that unified schema

• Enables an interface across all sources

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Downside• New source comes online with information not

covered by unified schema

• Have to make changes to all mappings to ensure accurate coverage

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New Approach: Pivoting• Marking up a single source, versus pushing all to a

single schema

• Use a pivot instead to situate metadata for integration

• Essentially means that the interface does the heavy lifting of integration

• Reduced effort by domain experts

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Interface Video

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Interface Video• Find a composer

• See all copyists of their manuscripts

• Choose a copyist and see which other composers that copyist has worked on

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Thank you

http://ecs.soton.ac.uk/projects/musicspace

ds@ecs.soton.ac.uk