Provenance and Uncertainty in Linked Art
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Presentation for Symposium held by Leuphana University on Linked Data descriptions of uncertainty and provenance of artwork, using Linked Art.
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Provenance and Uncertainty in Linked Art
- 1. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Linked Art, Provenance and Uncertainty https://linked.art/ Robert Sanderson Director for Cultural Heritage Metadata robert.sanderson@yale.edu @azaroth42
- 2. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Why Linked Art? https://en.wikipedia.org/wiki/usability Cultural Heritage data needs to be FAIR: • Findable – Persistent identifier and metadata • Accessible – (Persistently) retrievable • Interoperable – Uses shared standards, links out • Reusable – Accurate, open license and standards Linked Art provides a Standards based metadata profile, … designed for Usabilty and ease of implementation, … which are prerequisites for Sustainability
- 3. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu • Conceptual Model • Abstract way to think about the world, holistically, consistently and coherently • Ontology • Shared set of terms to encode that thinking in a logical, machine-actionable way • Vocabulary • Curated set of sub-domain specific terms, to make the ontology more concrete encodes refines Model Ontology Vocabulary Abstraction Standards
- 4. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu encodes refines specialized by available by Model Ontology Vocabulary Profile API Implementation Standards A Profile is a selection of appropriate abstractions, to encode the scope of what can be described. An API is a selection of appropriate technologies, to give access to the data managed using the profile.
- 5. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Linked Art Profile • Domain: Cultural Heritage, especially Artworks • Model: CIDOC Conceptual Reference Model • Ontology: RDF encoding of CRM 7.1, plus extensions • Vocabulary: Getty AAT, plus minimal extensions • Format: JSON-LD with 10 primary document boundaries • Target: 90% of the use cases with 10% of the effort https://linked.art/
- 6. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu What is Data Usability? … usability is the degree to which [a thing] can be used by specified consumers to achieve [their] quantified objectives with effectiveness, efficiency, and satisfaction in a quantified context of use. who what how where Usability is dependent on the Audience https://en.wikipedia.org/wiki/usability “ ”
- 10. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Progressive Enhancement • Data for: Humans - Strings • Separate entities, with searchable textual descriptions • Data for: Machines - Structured • Entities with machine-processable, comparable values • Data for: The Graph - d’Stributed • Entities are connected (within and across systems) • Data for: Research - Stringent • Sufficient accuracy and comprehensiveness to answer research questions from aggregated data Human Machine Graph Research
- 11. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Linked Art Provenance Model https://linked.art/model/provenance/
- 15. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Purchase by Knoedler from Unknown
- 18. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Acquisition Use Cases • Purchases: As in the example • Exchanges: Transfers of ownership of objects, no payments • Gifts: Only one Acquisition, nothing in return • Returns: Object is returned to seller (maybe bad credit?) • Multiple objects at once by repeating the pattern
- 20. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Custody Use Cases • Loans: e.g. for exhibitions • Permanent Loans: For display, with no return timeframe • Losses/Thefts: Transfer of custody to no one (but not transfer of ownership … they’re still the legal owner, even if they don’t know where it is) • Ownership vs Custody: Museum is the owner, Department has custody of it (and is part of the Museum) • Multiple objects at once by repeating the pattern
- 22. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Encounter Use Cases • Discovery of a Fossil: e.g. no production event • Rediscovery of a lost Object: e.g. statue in the sea • Inventory taking: e.g. curator/collector “encountered” the object even if no state of the world changed. • Physical co-location of agent and object: e.g. artist encountered objects at an exhibition, which then went on to affect their work
- 23. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Other Use Cases Covered • Commissions, Promised Gifts: Obligation of future action • Transfer of Rights: e.g. performance rights, copyright • Transfer of Partial Ownership: e.g. asymmetrical shared ownership (shares in the value of an object) • Physical Location: Movement between two places, rather than transfer of rights or currency • Auctions: A documented structure for sale by auction
- 24. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Uncertainty in Linked Art https://linked.art/model/assertion/
- 26. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Certainty? • Accuracy: Does the data correctly represent the state of the real world for the things it describes? (Objective) • Certainty: Belief of the Publisher as to the extent of the accuracy of the data. (Subjective) • Utility: Belief of the Researcher that the data is useful for fulfilling their current information need. (Subjective, context specific)
- 27. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Data Accuracy, Certainty and Utility
- 29. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Certain, Inaccurate, Not Useful https://search.library.yale.edu/catalog/1781761 (now corrected)
- 30. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Certain, Inaccurate, Not Useful Image from https://en.wikipedia.org/wiki/Blake_Edwards
- 31. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Relations between Actors • Confidence: Belief of the consumer in the current and past competence of the publisher (accuracy of data) • Trust: Belief of the consumer in the current and future benevolence of the publisher (availability of accurate data)
- 33. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Un/Certainty • Probably: Belief of the publisher that the data is likely to be accurate • Possibly: Belief of the publisher that the data is unlikely to be accurate • Formerly: Previously, the publisher believed that the data was accurate, but no longer has that belief
- 34. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Meta-Meta-Data We need to reify the knowledge, such that we can make further assertions about it: Attribute Assignment
- 35. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Meta-Meta-Data We can also add further meta-knowledge, such as when and who had the belief
- 36. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Single Assertions Each Attribute Assignment covers only a single assertion:
- 39. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Meta-Meta-Data Uncertainty pushes completeness up and usability down.
- 40. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Why Is It Hard? Consuming systems must … • Look in multiple places for the same information • More processing, more code, more developer knowledge • Understand the vocabulary of un/certainty levels • What processing needs to occur? • How can the structure be displayed? • Be able to merge metadata and metametadata • When appropriate, based on certainty and use cases
- 41. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Discussion Question: Why Make It Hard? • Data for: Humans - Strings • Separate entities, with searchable textual descriptions • Data for: Machines - Structured • Entities with machine-processable, comparable values Human Machine What is the requirement for structured, rather than string, data for uncertainty?
Notas do Editor
- Conflation of three separable features.
- Looking for hair in materials for an analysis of human remains in a collection, then this record is very useful – high utility for that research question, but low for most others given the uncertain (but perhaps accurate) information.
- Looking for the oldest person in our data… 39 trillion years old.
- When we talk about trust, we often mean confidence.
- When we talk about trust, we often mean confidence.
- When we talk about trust, we often mean confidence.