The Provenance of Madame Bonnier: Museum Linked Data

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Transcript of The Provenance of Madame Bonnier: Museum Linked Data

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Robert Sanderson

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The Provenance of Madame Bonnier:Museum Linked Data

With Thanks To:

Ruth CuadraBrenda PodemskiDavid Newbury

Kelly DavisJoan Cobb

Vladimir Alexiev…

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ance • Case Study: Madame Bonnier

• Data Models: Integration of Object and Provenance• Lessons Learnt• Data Ownership vs Stewardship• Data Quality• Model Alignment• Structure or Interaction?

Overview

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ance Portrait of Madame Bonnier de

la Mosson as DianaPaintingJean-Marc Nattier (1685 – 1766)France, 1742Oil on Canvas129.5 x 96.8 cm77.PA.87Getty MuseumOn View: South Pavilion, S202

Madame Bonnier

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Madame Bonnier: Provenance

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Madame Bonnier: Provenance

Museum Provenance Index

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Madame Bonnier: Provenance?

Provenance Index Provenance Index

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Madame Bonnier: Provenance?!

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Error in the Data :(

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Diana At Some Point

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Diana At Some Point

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Diana At Some Point

Ran out of red paint?

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ance Portrait of Madame Bonnier de

la Mosson as DianaPaintingJean-Marc Nattier (1685 – 1766)France, 1742Oil on Canvas129.5 x 96.8 cm77.PA.87Getty MuseumOn View: South Pavilion, S202

Madame Bonnier

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Museum Data Model: Description

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Museum Data Model: Provenance

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Museum Data Model: Complete

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Object Data Model: Complete

Start of Provenance

Current Provenance

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General Provenance Model

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Auctions Model

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Lessons Learnt

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Data Ownership vs StewardshipProvenance Index makes assertions about others’

objects (including Museum)

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Data Ownership vs StewardshipProvenance Index makes assertions about others’

objects (including Museum)

Authoritative about the documents … … but not about the objects’ provenance

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Data Ownership vs StewardshipProvenance Index makes assertions about others’

objects (including Museum)

Authoritative about the documents … … but not about the objects’ provenance

=> Acting as a steward for the provenance, not the owner of it

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Linked Data makes errors easier to find … … but doesn’t correct them for you

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Linked Data makes errors easier to find … … but doesn’t correct them for you

Quality of LOD is limited by quality of non LOD input, for purpose of linking

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Linked Data makes errors easier to find … … but doesn’t correct them for you

Quality of LOD is limited by quality of non LOD input, for purpose of linking

Beware false precision=> Automatic inferencing is risky

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Model AlignmentAligning models is hard, while avoiding false

precision!

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Model AlignmentAligning models is hard, while avoiding false

precision!

Need to get over “need” for 100% precision, 100% coverage, 100% completeness

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Model AlignmentAligning models is hard, while avoiding false

precision!

Need to get over “need” for 100% precision, 100% coverage, 100% completeness

=> Start somewhere, then implement, learn, correct, and iterate

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Structure or InteractionLinked Data as a Data Model … provides single structure to implement, query … acceptable if complete and accurate

Linked Data as an API … provides data to integrate, use … acceptable if understandable and usable

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Structure or InteractionLinked Data as a Data Model … provides single structure to implement, query … acceptable if complete and accurate

Linked Data as an API … provides data to integrate, use … acceptable if understandable and usable

=> As completeness increases, usability decreases

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If it’s not usable, why are we doing it?

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Need to focus on our goals … not on religious wars around ontologies

Need to find the balance between accuracy and usability

Need to recognize this is HARD and we’re not going to get it right the first time. Or the second.

Need to keep on keeping on :)

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Rob Sanderson / rsanderson@getty.edu

Thank You!