SlideShare une entreprise Scribd logo
1  sur  70
Télécharger pour lire hors ligne
Moving Beyond Ontologies
Linked Data in Production
David Newbury
Assistant Director, Software and User Experience, Getty
CNI Spring Project Briefings, March 26, 2024
Thank you for inviting me to present today.
I am a technologist working in the cultural heritage sector.
I lead the public digital team, developing applications to support Getty’s mission.
2
Introduction: David Newbury
I work for Getty in Los Angeles, which is a
library/archive/museum/research center.
One of our major areas of digital leadership is
in the use of Linked Data for cultural
heritage.
3
Introduction: Getty
Linked Open Data is a set of technologies that
attempt to translate some of the best
practices of the Web for use with structured
data:
● The use of URLs as identifiers
● Networks of information, not tables
● Formal, shared standards for description
4
Introduction: What is Linked Open Data?
As it turns out, Linked Data is not wildly successful.
5
Introduction: Linked Data is (mostly) Dead.
But we all still talk about it a lot.
Linked Data’s appeal in cultural heritage is a
technological solution to a social problem:
Cheap storage, ubiquitous connectivity, and
search algorithms recontextualize the labor
behind cultural heritage data work.
6
Introduction: Provocation
Mass digitization, computational metadata
generation, and decades of catalouging mean
that our institutions have more data to provide
than we have the ability to provide context for.
Data overload and limited user attention are
the collections access problems of the next
decade.
7
Introduction: Cheap Storage
By User5515 - Own work, CC BY 3.0,
https://commons.wikimedia.org/w/index.php?curid=114332790
Our always-connected culture means that our
collections are increasingly seen as part of a
single digital ecosystem—
And the questions that are being asked require
information that extends beyond the
boundaries of any one institution.
8
Introduction: Ubiquitous Connectivity
And commercial tools have given users the
expectation that information is available for the
asking—
Eliding the labor and capital needed to create,
curate, and maintain that information.
9
Introduction: Search Algorithms
Linked Data has been seen as a solution:
It provide structures that manage the scale of data we create,
identifiers that maintain authority in a globally distributed environment,
and ontologies that enable complex data retrieval across datasets.
10
Introduction: The Magic Bullet
What came before:
Laying the Groundwork
11
In 2014, the Getty Vocabularies were
launched as Linked Data.
This, alongside the work at Yale Center for
British Art, Rijksmuseum, and the British
Museum, demonstrated the feasibility of
LOD within the museum community.
12
Laying the Groundwork: Getty Vocabularies
The archives of the Carnegie Museum of Art’s
Film Department launched in 2014.
The animating question was:
What would happen if you treated the
relationships between events, archival
material, people, and artwork as the
essential element, not the objects?
13
Laying the Groundwork: Carnegie Museum Archives
In 2017, the American Art Collaborative
launched.
It used these same principles to highlight
connections across 14 institutions and
152,000 items—using Getty’s Vocabularies as
a bridging structure between institutions.
14
Laying the Groundwork: American Art Collaborative
One of the most lasting outcomes of the
American Art Collaborative was Linked.Art,
the shared data model that connected
institutions.
15
Laying the Groundwork: Linked Art
What we’ve done:
Getty’s Digital Ecosystem
16
Getty has been doing Linked Data since 2014,
starting with the Getty Vocabularies.
It’s a thesaurus of concepts, people, and
places used for cataloging across many
institutions.
17
Getty’s Linked Data: Getty Vocabularies
Since then, we’ve moved most of our major
systems to use Linked Data—including our
archives…
18
Getty’s Linked Data: Archival Records
Since then, we’ve moved most of our major
systems to use Linked Data—including our
archives…
… and our museum collection.
19
Getty’s Linked Data: Archival Records
It’s used for onsite visitor experiences
via our audio guide…
20
Getty’s Linked Data: Audio Guide
It’s used for onsite visitor
experiences
via our audio guide…
…and to provide novel interfaces for
exploration of our materials.
21
Getty’s Linked Data: 12 Sunsets
It’s also used by third parties: both large, like
Google Arts & Culture…
22
Getty’s Linked Data: Google Arts & Culture
It’s also used by third parties: both large, like
Google Arts & Culture…
…and small, like this project by the Cultural
Office of the Embassy of Spain.
23
Getty’s Linked Data: Spanish Art in the US
We’ve also built a complex, powerful digital
infrastructure to support this work—millions
of records in a single shared data model,
pulling from a wide collection of systems of
record.
24
Getty’s Linked Data: APIs
Under the hood:
Linked Data & the Everything API
25
26
Data Flow: How we wish it was
Staff Interface Public Website
These systems support people.
Digital infrastructure is designed to use computers to empower people to be
more effective at meeting the mission of the organization.
28
Getty’s Linked Data: User Needs
Catalogers need systems that match their
workflows—and different disciplines have
different needs.
Our infrastructure needed to not be tied to
any particular backend system.
29
Getty’s Linked Data: User Needs
Cataloging
System Support
Engineering
Access Research
Engineers need to get data in and out of
systems, using patterns and practices that
they already know how to use.
30
Getty’s Linked Data: User Needs
Cataloging
System Support
Engineering
Access Research
System admins just don’t want you to break
their stuff.
Pulling data out of systems on demand usually
breaks stuff.
31
Getty’s Linked Data: User Needs
Cataloging
System Support
Engineering
Access Research
Most end users are looking for content—they
want to learn what we know on a given topic.
This may be professional scholarship or it
might be looking for pictures—both are
examples of information-seeking behaviours.
32
Getty’s Linked Data: User Needs
Cataloging
System Support
Engineering
Access Research
And some researchers want to find questions
that haven’t been asked before—to find new
connections or patterns in the data that
others have overlooked.
33
Getty’s Linked Data: User Needs
Cataloging
System Support
Engineering
Access Research
Meeting the needs of catalogers is mostly not
my problem.
There are high-quality, professional tools that
work within the disciplinary training of the
field.
34
Getty’s Linked Data: The LOD Gateway
Providing access to that data, though, often
requires recontextualization:
Changing the conceptual lens from one
focused on staff efficiencies to one focused on
user’s needs.
35
Getty’s Linked Data: The LOD Gateway
Doing so requires combining data from
multiple systems and multiple workflows into
a new record.
This combining—or linking—of data has
tradeoffs.
36
Getty’s Linked Data: The LOD Gateway
Imagine a record for the painting Irises.
37
Getty’s Linked Data: The LOD Gateway
And a second record, this one for Van Gogh.
38
Getty’s Linked Data: The LOD Gateway
These could be seen as two separate documents:
39
Getty’s Linked Data: The LOD Gateway
"@context":
"https://linked.art/ns/v1/linked-art.json",
"id": "person/1",
"type": "Person",
"identified_by": {
"id": "person/1/name",
"type": "Name",
"content": "Vincent Van Gogh"
}
"@context":
"https://linked.art/ns/v1/linked-art.json",
"id": "object/1",
"type": "HumanMadeObject",
"identified_by": {
"id": "object/1/name",
"type": "Name",
"content": "Irises"
},
"produced_by": {
"id": "object/1/production",
"carried_out_by": {"id":"person/1"}
}
Or as a single graph.
40
Getty’s Linked Data: The LOD Gateway
From the point of view of the data, these
are equivalent—they contain the same facts.
But from a usability perspective, they make
different things easy or hard.
41
Getty’s Linked Data: The LOD Gateway
"@context":
"https://linked.art/ns/v1/linked-
art.json",
"id": "person/1",
"type": "Person",
"identified_by": {
"id": "person/1/name",
"type": "Name",
"content": "Vincent
Van Gogh"
}
"@context":
"https://linked.art/ns/v1/linked-art.js
on",
"id": "object/1",
"type": "HumanMadeObject",
"identified_by": {
"id": "object/1/name",
"type": "Name",
"content": "Irises"
},
"produced_by": {
"id": "object/1/production",
"carried_out_by": {"id":"person/1"}
}
Documents are optimized for Access:
They provide a specific set of data bundled
together by the data creator that provide all
the facts you need…given a specific context.
42
Documents: For Access and Discovery
"@context":
"https://linked.art/ns/v1/linked-art.json",
"id": "object/1",
"type": "HumanMadeObject",
"identified_by": {
"id": "object/1/name",
"type": "Name",
"content": "Irises"
},
"produced_by": {
"id": "object/1/production",
"carried_out_by": {"id":"person/1"}
}
Graphs, alternately, are optimized for querying:
Allowing a user to define a specific context based
on novel criteria and returning that subset of
facts.
43
Graphs: For Queries
44
Imagine two Questions:
“What objects does Getty have that have images larger than
1200px on the longest side that have been exhibited in both New
York and Paris and were created by artists who lived before 1850?
and
What’s the label info for Irises?
At the Getty, we have never asked:
“What objects does Getty have that have images larger than
1200px on the longest side that have been exhibited in both New
York and Paris and were created by artists who lived before 1850?
but we ask
What’s the label info for Irises?
Several thousand times a day.
45
Imagine two Questions:
Having an interface for documents lets us
provide a simple, easily understandable
record that maps well to known contexts.
This is important, because people usually
expect these contexts. It makes answering
common questions simple.
46
Documents: For Access and Discovery
Documents are also the way the internet
works: REST APIs, cache control, JSON,
webpages.
Using these well-known systems helps
developers make systems that are fast and
easy to build.
47
Documents: For Access and Discovery
Research is different—each scholar brings
their own question and their own context.
Meeting their need means empowering
them to draw their own boundaries within
the data.
48
Graphs: For Asking Questions
Doing so is complex—it moves the burden of
defining the relevant context to the user of
the data, not the creator of the data.
But it makes asking new questions possible,
even if it might be inefficient or complicated.
49
Graphs: For Asking Questions
We’ve built our infrastructure to allow for
both use cases:
A developer can create, update, and delete
documents, and behind the scenes it will
keep a graph in sync with those changes.
50
Meeting Both Needs
It also allows for synchronization across systems:
A editor changes a record, which means the API needs updated, which means the website
needs updated, and the search interfaces, and third-party systems…
51
Linked Data Infrastructure: Tracking Changes
The infrastructure uses uses the
W3C ActivityStream standard and are
implemented using the patterns from the
IIIF Change Discovery API.
Using standards makes it easy to build
integrations against changing data—both
within our organization and for external
aggregators.
52
Linked Data Infrastructure: ActivityStreams and Standards
For some kinds of data, it's also valuable to
also be able to see what has changed over
time for a given record.
To do so, our APIs also supports Memento,
the standard underneath the Internet
Archive.
53
Linked Data Infrastructure: ActivityStreams and Standards
This lets you automatically open older
versions of the record—providing an audit log
and the ability for scholars to understand
how knowledge changes over time.
54
LLinked Data Infrastructure: ActivityStreams and Standards
Cool Tech, Bro:
Why Does this Matter?
55
A Hard-won lesson:
No application that we’ve built required Linked Data.
56
Getty’s Linked Data: What we learned
A Hard-won lesson:
No application that we’ve built required Linked Data.
Which, if you think about it, makes sense. Each application has
a specific, known context with clear record boundaries.
57
Getty’s Linked Data: What we learned
A Hard-won lesson:
Different users have different contexts and need different affordances.
A shared, graph-based data model allows us to re-present the data in a way that
matches user’s varying models of the world via multiple interfaces.
58
Getty’s Linked Data: What we learned
As Simple as possible:
A shared data model also makes our developers more effective—eventually.
Building on top of web technology lets the engineering learning curve be gradual.
59
Getty’s Linked Data: What we learned
As Simple as possible:
Standards are valuable for interoperability—but also because
you don’t have to write all the documentation.
Nobody wants to write it, but you can’t work across institutions without it.
60
Getty’s Linked Data: What we learned
As Simple as possible:
Minimize complexity in the data model.
Data is for computers—text is for humans. Resist the urge to show off.
You can always add complexity—you can never take it away.
61
Getty’s Linked Data: What we learned
Disciplinary Misdeeds:
The hardest part of this will be change management.
Recontextualizing information across boundaries hides disciplinary labor—
and digital innovations can conflict with pre-digital best practices.
62
Getty’s Linked Data: What we learned
Evangelize and collaborate.
What makes cultural data interesting is not contained within any one institution.
It’s shared across our entire, world-wide community. We should work together.
Shared models and shared code make that easier.
63
Getty’s Linked Data: What we learned
Over the next several years, we’ll be
expanding our usage of this system:
This fall, we’ll launch a new version of the
Getty Provenance Index, adding in 22M
records of transactions between art
dealers.
This research-focused dataset will allow
new insights into collections around the
world—and into the art market as a whole.
64
What’s Next: Provenance Index
We’re beginning to plan the next iteration of
the Getty Vocabularies infrastructure:
Working to understand how the multiple
contexts of our audiences can be
supported—and how new ways of working
impact the platform.
65
What’s Next: Getty Vocabularies
And we’re using the platforms and
standards we’ve put in place to enable
collaboration across the field:
Working with the Smithsonian to provide
joint access and discovery for millions of
images from the photo morgue of
magazines such as Ebony and Jet.
66
What’s Next: Johnson Publishing Company Archives
Why do we do Linked Data?
The value is not in the technologies or the ontologies we use.
67
Getty’s Linked Data: What we learned
Why do we do Linked Data?
The value is in the ecosystem—information in varied context for different applications.
The value is in the audience—supporting user needs and conceptual models.
And it’s in the community—allowing data and code to be used beyond the Getty.
68
Getty’s Linked Data: What we learned
Why do we do Linked Data?
We do it for humans.
69
Getty’s Linked Data: What we learned
Contact me at dnewbury@getty.edu
Thank You.
David Newbury
Assistant Director, Software and User Experience, Getty
CNI Spring Project Briefings, March 26, 2024

Contenu connexe

Tendances

Introduction a IA(The first course of IA).pptx
Introduction a IA(The first course of IA).pptxIntroduction a IA(The first course of IA).pptx
Introduction a IA(The first course of IA).pptx
whatinspiremyworld
 

Tendances (20)

Fake news
Fake newsFake news
Fake news
 
Intelligence artificielle et système multi-agent
Intelligence artificielle et système multi-agentIntelligence artificielle et système multi-agent
Intelligence artificielle et système multi-agent
 
Techniques du data mining
Techniques du data miningTechniques du data mining
Techniques du data mining
 
Linked Art: Sustainable Cultural Knowledge through Linked Open Usable Data
Linked Art: Sustainable Cultural Knowledge through Linked Open Usable DataLinked Art: Sustainable Cultural Knowledge through Linked Open Usable Data
Linked Art: Sustainable Cultural Knowledge through Linked Open Usable Data
 
Traitement des images
Traitement des imagesTraitement des images
Traitement des images
 
ntroducing to the Power of Graph Technology
ntroducing to the Power of Graph Technologyntroducing to the Power of Graph Technology
ntroducing to the Power of Graph Technology
 
Utiliser les tablettes avec les seniors en bibliothèque
Utiliser les tablettes avec les seniors en bibliothèqueUtiliser les tablettes avec les seniors en bibliothèque
Utiliser les tablettes avec les seniors en bibliothèque
 
Big data - Cours d'introduction l Data-business
Big data - Cours d'introduction l Data-businessBig data - Cours d'introduction l Data-business
Big data - Cours d'introduction l Data-business
 
Agent intelligent
Agent intelligentAgent intelligent
Agent intelligent
 
Intelligence artificielle et soin.pptx
Intelligence artificielle et soin.pptxIntelligence artificielle et soin.pptx
Intelligence artificielle et soin.pptx
 
Vector Similarity Search & Indexing Methods
Vector Similarity Search & Indexing MethodsVector Similarity Search & Indexing Methods
Vector Similarity Search & Indexing Methods
 
Introduction a IA(The first course of IA).pptx
Introduction a IA(The first course of IA).pptxIntroduction a IA(The first course of IA).pptx
Introduction a IA(The first course of IA).pptx
 
Data-Centric Business Transformation Using Knowledge Graphs
Data-Centric Business Transformation Using Knowledge GraphsData-Centric Business Transformation Using Knowledge Graphs
Data-Centric Business Transformation Using Knowledge Graphs
 
Investigation des épidemies en milieu hospitalier
Investigation des épidemies en milieu hospitalierInvestigation des épidemies en milieu hospitalier
Investigation des épidemies en milieu hospitalier
 
Les 8 principes du management de la qualité
Les 8 principes du management de la qualitéLes 8 principes du management de la qualité
Les 8 principes du management de la qualité
 
Neo4j - Cas d'usages pour votre métier
Neo4j - Cas d'usages pour votre métierNeo4j - Cas d'usages pour votre métier
Neo4j - Cas d'usages pour votre métier
 
Tietosuoja ja sosiaalinen media
Tietosuoja ja sosiaalinen mediaTietosuoja ja sosiaalinen media
Tietosuoja ja sosiaalinen media
 
Médias sociaux : panorama, stratégies et influenceurs
Médias sociaux : panorama, stratégies et influenceursMédias sociaux : panorama, stratégies et influenceurs
Médias sociaux : panorama, stratégies et influenceurs
 
Chap VI 1-Elements de Pratique des SIG sous QGIS
Chap VI 1-Elements de Pratique des SIG sous QGISChap VI 1-Elements de Pratique des SIG sous QGIS
Chap VI 1-Elements de Pratique des SIG sous QGIS
 
communiquer-reseaux-sociaux.pptx
communiquer-reseaux-sociaux.pptxcommuniquer-reseaux-sociaux.pptx
communiquer-reseaux-sociaux.pptx
 

Similaire à Linked Data in Production: Moving Beyond Ontologies

Discovery event peter burnhill (aggregation as tactic)
Discovery event peter burnhill (aggregation as tactic)Discovery event peter burnhill (aggregation as tactic)
Discovery event peter burnhill (aggregation as tactic)
RDTF-Discovery
 
Towards the Discovery of Person-Level Data (SemStats, ISWC 2013) [2013.10]
Towards the Discovery of Person-Level Data (SemStats, ISWC 2013) [2013.10]Towards the Discovery of Person-Level Data (SemStats, ISWC 2013) [2013.10]
Towards the Discovery of Person-Level Data (SemStats, ISWC 2013) [2013.10]
Dr.-Ing. Thomas Hartmann
 
Data Science: History repeated? – The heritage of the Free and Open Source GI...
Data Science: History repeated? – The heritage of the Free and Open Source GI...Data Science: History repeated? – The heritage of the Free and Open Source GI...
Data Science: History repeated? – The heritage of the Free and Open Source GI...
Peter Löwe
 
The Semantic Web Exists. What Next?
The Semantic Web Exists. What Next?The Semantic Web Exists. What Next?
The Semantic Web Exists. What Next?
Anna Fensel
 

Similaire à Linked Data in Production: Moving Beyond Ontologies (20)

The LOD Gateway: Open Source Infrastructure for Linked Data
The LOD Gateway: Open Source Infrastructure for Linked DataThe LOD Gateway: Open Source Infrastructure for Linked Data
The LOD Gateway: Open Source Infrastructure for Linked Data
 
Can’t Pay, Won’t Pay, Don’t Pay: Delivering open science, a Digital Research...
Can’t Pay, Won’t Pay, Don’t Pay: Delivering open science,  a Digital Research...Can’t Pay, Won’t Pay, Don’t Pay: Delivering open science,  a Digital Research...
Can’t Pay, Won’t Pay, Don’t Pay: Delivering open science, a Digital Research...
 
Aggregation as tactic sm new
Aggregation as tactic sm newAggregation as tactic sm new
Aggregation as tactic sm new
 
Aggregation as Tactic
Aggregation as TacticAggregation as Tactic
Aggregation as Tactic
 
Discovery event peter burnhill (aggregation as tactic)
Discovery event peter burnhill (aggregation as tactic)Discovery event peter burnhill (aggregation as tactic)
Discovery event peter burnhill (aggregation as tactic)
 
Optimising Scientific Knowledge Transfer: How Collective Sensemaking Can Ena...
Optimising Scientific Knowledge Transfer: How Collective Sensemaking Can Ena...Optimising Scientific Knowledge Transfer: How Collective Sensemaking Can Ena...
Optimising Scientific Knowledge Transfer: How Collective Sensemaking Can Ena...
 
Towards long-term preservation of linked data - the PRELIDA project
Towards long-term preservation of linked data - the PRELIDA projectTowards long-term preservation of linked data - the PRELIDA project
Towards long-term preservation of linked data - the PRELIDA project
 
Collaborative Data Management at the University of California
Collaborative Data Management at the University of CaliforniaCollaborative Data Management at the University of California
Collaborative Data Management at the University of California
 
Towards the Discovery of Person-Level Data (SemStats, ISWC 2013) [2013.10]
Towards the Discovery of Person-Level Data (SemStats, ISWC 2013) [2013.10]Towards the Discovery of Person-Level Data (SemStats, ISWC 2013) [2013.10]
Towards the Discovery of Person-Level Data (SemStats, ISWC 2013) [2013.10]
 
WORLD CAT AS BIG DATA
WORLD CAT AS  BIG DATAWORLD CAT AS  BIG DATA
WORLD CAT AS BIG DATA
 
Enabling Smarter Cities through Internet of Things, Web of Data & Citizen Par...
Enabling Smarter Cities through Internet of Things, Web of Data & Citizen Par...Enabling Smarter Cities through Internet of Things, Web of Data & Citizen Par...
Enabling Smarter Cities through Internet of Things, Web of Data & Citizen Par...
 
What is eScience, and where does it go from here?
What is eScience, and where does it go from here?What is eScience, and where does it go from here?
What is eScience, and where does it go from here?
 
A distributed network of digital heritage information - Unesco/NDL India
A distributed network of digital heritage information - Unesco/NDL IndiaA distributed network of digital heritage information - Unesco/NDL India
A distributed network of digital heritage information - Unesco/NDL India
 
From Records to Data
From Records to DataFrom Records to Data
From Records to Data
 
Data Science: History repeated? – The heritage of the Free and Open Source GI...
Data Science: History repeated? – The heritage of the Free and Open Source GI...Data Science: History repeated? – The heritage of the Free and Open Source GI...
Data Science: History repeated? – The heritage of the Free and Open Source GI...
 
Rdaeu russia_fg_1_july2014_final
Rdaeu  russia_fg_1_july2014_finalRdaeu  russia_fg_1_july2014_final
Rdaeu russia_fg_1_july2014_final
 
Digital Humanities Workshop
Digital Humanities WorkshopDigital Humanities Workshop
Digital Humanities Workshop
 
From Records to Data with Recollection
From Records to Data with RecollectionFrom Records to Data with Recollection
From Records to Data with Recollection
 
Carpenter "The Future of the Scholarly Record"
Carpenter "The Future of the Scholarly Record"Carpenter "The Future of the Scholarly Record"
Carpenter "The Future of the Scholarly Record"
 
The Semantic Web Exists. What Next?
The Semantic Web Exists. What Next?The Semantic Web Exists. What Next?
The Semantic Web Exists. What Next?
 

Plus de David Newbury

IIIF For Small Projects
IIIF  For Small ProjectsIIIF  For Small Projects
IIIF For Small Projects
David Newbury
 

Plus de David Newbury (20)

Linked Data on a Budget
Linked Data on a BudgetLinked Data on a Budget
Linked Data on a Budget
 
USE ME: progressive integration of IIIF with new software services at the Getty
USE ME: progressive integration of IIIF with new software services at the GettyUSE ME: progressive integration of IIIF with new software services at the Getty
USE ME: progressive integration of IIIF with new software services at the Getty
 
IIIF Across Platforms | IIIF Community Call, January 2021
IIIF Across Platforms | IIIF Community Call, January 2021IIIF Across Platforms | IIIF Community Call, January 2021
IIIF Across Platforms | IIIF Community Call, January 2021
 
IIIF Canvases as First Class Citizens
IIIF Canvases as First Class CitizensIIIF Canvases as First Class Citizens
IIIF Canvases as First Class Citizens
 
IIIF and Linked Open Data: LODLAM 2020
IIIF and Linked Open Data: LODLAM 2020IIIF and Linked Open Data: LODLAM 2020
IIIF and Linked Open Data: LODLAM 2020
 
How to Fail Interdisciplinarily
How to Fail InterdisciplinarilyHow to Fail Interdisciplinarily
How to Fail Interdisciplinarily
 
Sharing Data Across Memory Institutions
Sharing Data Across Memory InstitutionsSharing Data Across Memory Institutions
Sharing Data Across Memory Institutions
 
Extending IIIF 3.0
Extending IIIF 3.0Extending IIIF 3.0
Extending IIIF 3.0
 
NDSR Learning Enrichment: Data Models and Linked Data
NDSR Learning Enrichment: Data Models and Linked DataNDSR Learning Enrichment: Data Models and Linked Data
NDSR Learning Enrichment: Data Models and Linked Data
 
Fuzzy Dates & the Digital Humanities
Fuzzy Dates & the Digital HumanitiesFuzzy Dates & the Digital Humanities
Fuzzy Dates & the Digital Humanities
 
Telling Stories with Data: Class Notes 2
Telling Stories with Data:  Class Notes 2Telling Stories with Data:  Class Notes 2
Telling Stories with Data: Class Notes 2
 
Telling Stories With Data: Class 1
Telling Stories With Data: Class 1Telling Stories With Data: Class 1
Telling Stories With Data: Class 1
 
21st Century Provenance: Lessons Learned Building Art Tracks
21st Century Provenance:  Lessons Learned Building Art Tracks21st Century Provenance:  Lessons Learned Building Art Tracks
21st Century Provenance: Lessons Learned Building Art Tracks
 
Art Tracks: From Provenance to Structured Data
Art Tracks: From Provenance to Structured DataArt Tracks: From Provenance to Structured Data
Art Tracks: From Provenance to Structured Data
 
Linked Data: Worse is Better
Linked Data:  Worse is BetterLinked Data:  Worse is Better
Linked Data: Worse is Better
 
Understanding D3
Understanding D3Understanding D3
Understanding D3
 
Art Tracks: A technical deep dive.
Art Tracks:  A technical deep dive.Art Tracks:  A technical deep dive.
Art Tracks: A technical deep dive.
 
Using Linked Data: American Art Collaborative, Oct. 3, 2016
Using Linked Data:  American Art Collaborative, Oct. 3, 2016Using Linked Data:  American Art Collaborative, Oct. 3, 2016
Using Linked Data: American Art Collaborative, Oct. 3, 2016
 
Data 101: Making Charts from Spreadsheets
Data 101: Making Charts from SpreadsheetsData 101: Making Charts from Spreadsheets
Data 101: Making Charts from Spreadsheets
 
IIIF For Small Projects
IIIF  For Small ProjectsIIIF  For Small Projects
IIIF For Small Projects
 

Dernier

Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 

Dernier (20)

Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
 
ADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptxADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptx
 
Introduction to use of FHIR Documents in ABDM
Introduction to use of FHIR Documents in ABDMIntroduction to use of FHIR Documents in ABDM
Introduction to use of FHIR Documents in ABDM
 
Navigating Identity and Access Management in the Modern Enterprise
Navigating Identity and Access Management in the Modern EnterpriseNavigating Identity and Access Management in the Modern Enterprise
Navigating Identity and Access Management in the Modern Enterprise
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..
 
WSO2 Micro Integrator for Enterprise Integration in a Decentralized, Microser...
WSO2 Micro Integrator for Enterprise Integration in a Decentralized, Microser...WSO2 Micro Integrator for Enterprise Integration in a Decentralized, Microser...
WSO2 Micro Integrator for Enterprise Integration in a Decentralized, Microser...
 
Less Is More: Utilizing Ballerina to Architect a Cloud Data Platform
Less Is More: Utilizing Ballerina to Architect a Cloud Data PlatformLess Is More: Utilizing Ballerina to Architect a Cloud Data Platform
Less Is More: Utilizing Ballerina to Architect a Cloud Data Platform
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
JohnPollard-hybrid-app-RailsConf2024.pptx
JohnPollard-hybrid-app-RailsConf2024.pptxJohnPollard-hybrid-app-RailsConf2024.pptx
JohnPollard-hybrid-app-RailsConf2024.pptx
 
Modernizing Legacy Systems Using Ballerina
Modernizing Legacy Systems Using BallerinaModernizing Legacy Systems Using Ballerina
Modernizing Legacy Systems Using Ballerina
 
Design Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxDesign Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptx
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
The Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightThe Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and Insight
 
Simplifying Mobile A11y Presentation.pptx
Simplifying Mobile A11y Presentation.pptxSimplifying Mobile A11y Presentation.pptx
Simplifying Mobile A11y Presentation.pptx
 

Linked Data in Production: Moving Beyond Ontologies

  • 1. Moving Beyond Ontologies Linked Data in Production David Newbury Assistant Director, Software and User Experience, Getty CNI Spring Project Briefings, March 26, 2024
  • 2. Thank you for inviting me to present today. I am a technologist working in the cultural heritage sector. I lead the public digital team, developing applications to support Getty’s mission. 2 Introduction: David Newbury
  • 3. I work for Getty in Los Angeles, which is a library/archive/museum/research center. One of our major areas of digital leadership is in the use of Linked Data for cultural heritage. 3 Introduction: Getty
  • 4. Linked Open Data is a set of technologies that attempt to translate some of the best practices of the Web for use with structured data: ● The use of URLs as identifiers ● Networks of information, not tables ● Formal, shared standards for description 4 Introduction: What is Linked Open Data?
  • 5. As it turns out, Linked Data is not wildly successful. 5 Introduction: Linked Data is (mostly) Dead. But we all still talk about it a lot.
  • 6. Linked Data’s appeal in cultural heritage is a technological solution to a social problem: Cheap storage, ubiquitous connectivity, and search algorithms recontextualize the labor behind cultural heritage data work. 6 Introduction: Provocation
  • 7. Mass digitization, computational metadata generation, and decades of catalouging mean that our institutions have more data to provide than we have the ability to provide context for. Data overload and limited user attention are the collections access problems of the next decade. 7 Introduction: Cheap Storage By User5515 - Own work, CC BY 3.0, https://commons.wikimedia.org/w/index.php?curid=114332790
  • 8. Our always-connected culture means that our collections are increasingly seen as part of a single digital ecosystem— And the questions that are being asked require information that extends beyond the boundaries of any one institution. 8 Introduction: Ubiquitous Connectivity
  • 9. And commercial tools have given users the expectation that information is available for the asking— Eliding the labor and capital needed to create, curate, and maintain that information. 9 Introduction: Search Algorithms
  • 10. Linked Data has been seen as a solution: It provide structures that manage the scale of data we create, identifiers that maintain authority in a globally distributed environment, and ontologies that enable complex data retrieval across datasets. 10 Introduction: The Magic Bullet
  • 11. What came before: Laying the Groundwork 11
  • 12. In 2014, the Getty Vocabularies were launched as Linked Data. This, alongside the work at Yale Center for British Art, Rijksmuseum, and the British Museum, demonstrated the feasibility of LOD within the museum community. 12 Laying the Groundwork: Getty Vocabularies
  • 13. The archives of the Carnegie Museum of Art’s Film Department launched in 2014. The animating question was: What would happen if you treated the relationships between events, archival material, people, and artwork as the essential element, not the objects? 13 Laying the Groundwork: Carnegie Museum Archives
  • 14. In 2017, the American Art Collaborative launched. It used these same principles to highlight connections across 14 institutions and 152,000 items—using Getty’s Vocabularies as a bridging structure between institutions. 14 Laying the Groundwork: American Art Collaborative
  • 15. One of the most lasting outcomes of the American Art Collaborative was Linked.Art, the shared data model that connected institutions. 15 Laying the Groundwork: Linked Art
  • 16. What we’ve done: Getty’s Digital Ecosystem 16
  • 17. Getty has been doing Linked Data since 2014, starting with the Getty Vocabularies. It’s a thesaurus of concepts, people, and places used for cataloging across many institutions. 17 Getty’s Linked Data: Getty Vocabularies
  • 18. Since then, we’ve moved most of our major systems to use Linked Data—including our archives… 18 Getty’s Linked Data: Archival Records
  • 19. Since then, we’ve moved most of our major systems to use Linked Data—including our archives… … and our museum collection. 19 Getty’s Linked Data: Archival Records
  • 20. It’s used for onsite visitor experiences via our audio guide… 20 Getty’s Linked Data: Audio Guide
  • 21. It’s used for onsite visitor experiences via our audio guide… …and to provide novel interfaces for exploration of our materials. 21 Getty’s Linked Data: 12 Sunsets
  • 22. It’s also used by third parties: both large, like Google Arts & Culture… 22 Getty’s Linked Data: Google Arts & Culture
  • 23. It’s also used by third parties: both large, like Google Arts & Culture… …and small, like this project by the Cultural Office of the Embassy of Spain. 23 Getty’s Linked Data: Spanish Art in the US
  • 24. We’ve also built a complex, powerful digital infrastructure to support this work—millions of records in a single shared data model, pulling from a wide collection of systems of record. 24 Getty’s Linked Data: APIs
  • 25. Under the hood: Linked Data & the Everything API 25
  • 26. 26 Data Flow: How we wish it was Staff Interface Public Website
  • 27.
  • 28. These systems support people. Digital infrastructure is designed to use computers to empower people to be more effective at meeting the mission of the organization. 28 Getty’s Linked Data: User Needs
  • 29. Catalogers need systems that match their workflows—and different disciplines have different needs. Our infrastructure needed to not be tied to any particular backend system. 29 Getty’s Linked Data: User Needs Cataloging System Support Engineering Access Research
  • 30. Engineers need to get data in and out of systems, using patterns and practices that they already know how to use. 30 Getty’s Linked Data: User Needs Cataloging System Support Engineering Access Research
  • 31. System admins just don’t want you to break their stuff. Pulling data out of systems on demand usually breaks stuff. 31 Getty’s Linked Data: User Needs Cataloging System Support Engineering Access Research
  • 32. Most end users are looking for content—they want to learn what we know on a given topic. This may be professional scholarship or it might be looking for pictures—both are examples of information-seeking behaviours. 32 Getty’s Linked Data: User Needs Cataloging System Support Engineering Access Research
  • 33. And some researchers want to find questions that haven’t been asked before—to find new connections or patterns in the data that others have overlooked. 33 Getty’s Linked Data: User Needs Cataloging System Support Engineering Access Research
  • 34. Meeting the needs of catalogers is mostly not my problem. There are high-quality, professional tools that work within the disciplinary training of the field. 34 Getty’s Linked Data: The LOD Gateway
  • 35. Providing access to that data, though, often requires recontextualization: Changing the conceptual lens from one focused on staff efficiencies to one focused on user’s needs. 35 Getty’s Linked Data: The LOD Gateway
  • 36. Doing so requires combining data from multiple systems and multiple workflows into a new record. This combining—or linking—of data has tradeoffs. 36 Getty’s Linked Data: The LOD Gateway
  • 37. Imagine a record for the painting Irises. 37 Getty’s Linked Data: The LOD Gateway
  • 38. And a second record, this one for Van Gogh. 38 Getty’s Linked Data: The LOD Gateway
  • 39. These could be seen as two separate documents: 39 Getty’s Linked Data: The LOD Gateway "@context": "https://linked.art/ns/v1/linked-art.json", "id": "person/1", "type": "Person", "identified_by": { "id": "person/1/name", "type": "Name", "content": "Vincent Van Gogh" } "@context": "https://linked.art/ns/v1/linked-art.json", "id": "object/1", "type": "HumanMadeObject", "identified_by": { "id": "object/1/name", "type": "Name", "content": "Irises" }, "produced_by": { "id": "object/1/production", "carried_out_by": {"id":"person/1"} }
  • 40. Or as a single graph. 40 Getty’s Linked Data: The LOD Gateway
  • 41. From the point of view of the data, these are equivalent—they contain the same facts. But from a usability perspective, they make different things easy or hard. 41 Getty’s Linked Data: The LOD Gateway "@context": "https://linked.art/ns/v1/linked- art.json", "id": "person/1", "type": "Person", "identified_by": { "id": "person/1/name", "type": "Name", "content": "Vincent Van Gogh" } "@context": "https://linked.art/ns/v1/linked-art.js on", "id": "object/1", "type": "HumanMadeObject", "identified_by": { "id": "object/1/name", "type": "Name", "content": "Irises" }, "produced_by": { "id": "object/1/production", "carried_out_by": {"id":"person/1"} }
  • 42. Documents are optimized for Access: They provide a specific set of data bundled together by the data creator that provide all the facts you need…given a specific context. 42 Documents: For Access and Discovery "@context": "https://linked.art/ns/v1/linked-art.json", "id": "object/1", "type": "HumanMadeObject", "identified_by": { "id": "object/1/name", "type": "Name", "content": "Irises" }, "produced_by": { "id": "object/1/production", "carried_out_by": {"id":"person/1"} }
  • 43. Graphs, alternately, are optimized for querying: Allowing a user to define a specific context based on novel criteria and returning that subset of facts. 43 Graphs: For Queries
  • 44. 44 Imagine two Questions: “What objects does Getty have that have images larger than 1200px on the longest side that have been exhibited in both New York and Paris and were created by artists who lived before 1850? and What’s the label info for Irises?
  • 45. At the Getty, we have never asked: “What objects does Getty have that have images larger than 1200px on the longest side that have been exhibited in both New York and Paris and were created by artists who lived before 1850? but we ask What’s the label info for Irises? Several thousand times a day. 45 Imagine two Questions:
  • 46. Having an interface for documents lets us provide a simple, easily understandable record that maps well to known contexts. This is important, because people usually expect these contexts. It makes answering common questions simple. 46 Documents: For Access and Discovery
  • 47. Documents are also the way the internet works: REST APIs, cache control, JSON, webpages. Using these well-known systems helps developers make systems that are fast and easy to build. 47 Documents: For Access and Discovery
  • 48. Research is different—each scholar brings their own question and their own context. Meeting their need means empowering them to draw their own boundaries within the data. 48 Graphs: For Asking Questions
  • 49. Doing so is complex—it moves the burden of defining the relevant context to the user of the data, not the creator of the data. But it makes asking new questions possible, even if it might be inefficient or complicated. 49 Graphs: For Asking Questions
  • 50. We’ve built our infrastructure to allow for both use cases: A developer can create, update, and delete documents, and behind the scenes it will keep a graph in sync with those changes. 50 Meeting Both Needs
  • 51. It also allows for synchronization across systems: A editor changes a record, which means the API needs updated, which means the website needs updated, and the search interfaces, and third-party systems… 51 Linked Data Infrastructure: Tracking Changes
  • 52. The infrastructure uses uses the W3C ActivityStream standard and are implemented using the patterns from the IIIF Change Discovery API. Using standards makes it easy to build integrations against changing data—both within our organization and for external aggregators. 52 Linked Data Infrastructure: ActivityStreams and Standards
  • 53. For some kinds of data, it's also valuable to also be able to see what has changed over time for a given record. To do so, our APIs also supports Memento, the standard underneath the Internet Archive. 53 Linked Data Infrastructure: ActivityStreams and Standards
  • 54. This lets you automatically open older versions of the record—providing an audit log and the ability for scholars to understand how knowledge changes over time. 54 LLinked Data Infrastructure: ActivityStreams and Standards
  • 55. Cool Tech, Bro: Why Does this Matter? 55
  • 56. A Hard-won lesson: No application that we’ve built required Linked Data. 56 Getty’s Linked Data: What we learned
  • 57. A Hard-won lesson: No application that we’ve built required Linked Data. Which, if you think about it, makes sense. Each application has a specific, known context with clear record boundaries. 57 Getty’s Linked Data: What we learned
  • 58. A Hard-won lesson: Different users have different contexts and need different affordances. A shared, graph-based data model allows us to re-present the data in a way that matches user’s varying models of the world via multiple interfaces. 58 Getty’s Linked Data: What we learned
  • 59. As Simple as possible: A shared data model also makes our developers more effective—eventually. Building on top of web technology lets the engineering learning curve be gradual. 59 Getty’s Linked Data: What we learned
  • 60. As Simple as possible: Standards are valuable for interoperability—but also because you don’t have to write all the documentation. Nobody wants to write it, but you can’t work across institutions without it. 60 Getty’s Linked Data: What we learned
  • 61. As Simple as possible: Minimize complexity in the data model. Data is for computers—text is for humans. Resist the urge to show off. You can always add complexity—you can never take it away. 61 Getty’s Linked Data: What we learned
  • 62. Disciplinary Misdeeds: The hardest part of this will be change management. Recontextualizing information across boundaries hides disciplinary labor— and digital innovations can conflict with pre-digital best practices. 62 Getty’s Linked Data: What we learned
  • 63. Evangelize and collaborate. What makes cultural data interesting is not contained within any one institution. It’s shared across our entire, world-wide community. We should work together. Shared models and shared code make that easier. 63 Getty’s Linked Data: What we learned
  • 64. Over the next several years, we’ll be expanding our usage of this system: This fall, we’ll launch a new version of the Getty Provenance Index, adding in 22M records of transactions between art dealers. This research-focused dataset will allow new insights into collections around the world—and into the art market as a whole. 64 What’s Next: Provenance Index
  • 65. We’re beginning to plan the next iteration of the Getty Vocabularies infrastructure: Working to understand how the multiple contexts of our audiences can be supported—and how new ways of working impact the platform. 65 What’s Next: Getty Vocabularies
  • 66. And we’re using the platforms and standards we’ve put in place to enable collaboration across the field: Working with the Smithsonian to provide joint access and discovery for millions of images from the photo morgue of magazines such as Ebony and Jet. 66 What’s Next: Johnson Publishing Company Archives
  • 67. Why do we do Linked Data? The value is not in the technologies or the ontologies we use. 67 Getty’s Linked Data: What we learned
  • 68. Why do we do Linked Data? The value is in the ecosystem—information in varied context for different applications. The value is in the audience—supporting user needs and conceptual models. And it’s in the community—allowing data and code to be used beyond the Getty. 68 Getty’s Linked Data: What we learned
  • 69. Why do we do Linked Data? We do it for humans. 69 Getty’s Linked Data: What we learned
  • 70. Contact me at dnewbury@getty.edu Thank You. David Newbury Assistant Director, Software and User Experience, Getty CNI Spring Project Briefings, March 26, 2024