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
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
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
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)
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?
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.