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Using Pivots to Explore Heterogeneous Collections A Case Study in Musicology Daniel Alexander Smith 8 December 2009.

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Presentation on theme: "Using Pivots to Explore Heterogeneous Collections A Case Study in Musicology Daniel Alexander Smith 8 December 2009."— Presentation transcript:

1 Using Pivots to Explore Heterogeneous Collections A Case Study in Musicology Daniel Alexander Smith 8 December 2009

2 musicSpace IAM Group, School of Electronics and Computer Science Music, School of Humanities 2

3 Outline How musicologists use data Limitations of existing approaches Our data extraction and integration methodology Interface walkthrough 3

4 musicSpace Tasks Triage data partners sources Extract information Map data sources to schemas/ontologies Produce interface over aggregated data Customise interface based on feedback 4

5 Data in Musicology 5

6 Musicologists consult many data sources 6

7 ... but what if they could use just one? 7

8 Intractable research questions Which scribes have created manuscripts of a composers 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? 8

9 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 9

10 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 10

11 Why they are intractable (3) Insufficient granualrity of metadata and/or search option Solution: Increase granularity 11

12 Metadata Extraction 12

13 Previous work Comb-e-chem modelled Chemistry data We use similar approach Translated this work to the arts Musicology modelled using Semantic Web technologies 13

14 Musicology Data Sources Disparate data How to pull them together and view on demand 14

15 15 Public British Library Music Collections British Library Sound Archive Cecilia Copac RISM (UK and Ireland) Commercial Grove Music Online Naxos Music Library RILM Future? Alexander Street Press Music Online CHARM Personal datasets musicSpace Data Partners

16 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 16

17 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 17

18 Grove Works Lists Source Data 18

19 Works List Metadata Tool 19

20 Data Integration

21 Integration Domain Expert + Technologist partnership This will be case for some time now Technology to best automate tasks to make domain experts job less onerous 21

22 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 22

23 Downside New source comes online with information not covered by unified schema Have to make changes to all mappings to ensure accurate coverage 23

24 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 24

25 Interface Video 25

26 Interface Video Find a composer See all copyists of their manuscripts Choose a copyist and see which other composers that copyist has worked on 26

27 27

28 Thank you


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