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Using Pivots to Explore Heterogeneous Collections. A Case Study in Musicology. Daniel Alexander Smith 8 December 2009. musicSpace. IAM Group, School of Electronics and Computer Science Music, School of Humanities. Outline. How musicologists use data

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using pivots to explore heterogeneous collections

Using Pivots to Explore Heterogeneous Collections

A Case Study in Musicology

Daniel Alexander Smith8 December 2009


  • IAM Group, School of Electronics and Computer Science
  • Music, School of Humanities
  • How musicologists use data
  • Limitations of existing approaches
  • Our data extraction and integration methodology
  • Interface walkthrough
musicspace tasks
musicSpace Tasks
  • Triage data partners sources
  • Extract information
  • Map data sources to schemas/ontologies
  • Produce interface over aggregated data
  • Customise interface based on feedback
intractable research questions
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?
why they are intractable 1
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
why they are intractable 2
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
why they are intractable 3
Why they are intractable (3)
  • Insufficient granualrity of metadata and/or search option
  • Solution: Increase granularity
previous work
Previous work
  • Comb-e-chem modelled Chemistry data
  • We use similar approach
  • Translated this work to the arts
  • Musicology modelled using Semantic Web technologies
musicology data sources
Musicology Data Sources
  • Disparate data
  • How to pull them together and view on demand
data and info management problems
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
grove extraction example
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
  • 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
metadata mapping
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
  • New source comes online with information not covered by unified schema
  • Have to make changes to all mappings to ensure accurate coverage
new approach pivoting
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
interface video1
Interface Video
  • Find a composer
  • See all copyists of their manuscripts
  • Choose a copyist and see which other composers that copyist has worked on