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![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
“ ”](https://image.slidesharecdn.com/linked-art-uncertainty-220630130501-7910ffca/75/Provenance-and-Uncertainty-in-Linked-Art-6-2048.jpg)
Uploaded byRobert Sanderson
Provenance and Uncertainty in Linked Art
The document discusses the importance of provenance and uncertainty in cultural heritage metadata, emphasizing the need for standards to ensure data is findable, accessible, interoperable, and reusable. It outlines a linked art profile that focuses on usability and describes various use cases related to ownership, custody, and the complexities of data accuracy and certainty. The text highlights the challenges in providing clear data due to varying levels of certainty and the need for structured data for both human and machine usability.
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Provenance and Uncertainty in Linked Art
- 1. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Linked Art, Provenance andUncertainty 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 CulturalHeritage 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 availableby 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 DataUsability? … 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 “ ”
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- 10. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Progressive Enhancement • Datafor: 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 ProvenanceModel https://linked.art/model/provenance/
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- 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
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- 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
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- 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 CasesCovered • 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 LinkedArt https://linked.art/model/assertion/
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- 26. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Certainty? • Accuracy: Doesthe 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)
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- 29. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Certain, Inaccurate, NotUseful https://search.library.yale.edu/catalog/1781761 (now corrected)
- 30. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Certain, Inaccurate, NotUseful 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)
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- 33. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Un/Certainty • Probably: Beliefof 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 toreify 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 alsoadd further meta-knowledge, such as when and who had the belief
- 36. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Single Assertions Each AttributeAssignment covers only a single assertion:
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- 40. Provenance and Uncertainty @azaroth42 robert. sanderson @yale.edu Why Is ItHard? 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: WhyMake 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?
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Editor's Notes
- #27 Conflation of three separable features.
- #29 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.
- #30 Looking for the oldest person in our data… 39 trillion years old.
- #32 When we talk about trust, we often mean confidence.
- #34 When we talk about trust, we often mean confidence.
- #41 When we talk about trust, we often mean confidence.