The document discusses the importance of reconciling linked open data, emphasizing the role of URIs in identifying entities and the challenges involved in maintaining and discovering these URIs. It presents two main options for reconciliation—algorithmic discovery of shared entities or centralized authority for identifying matched entities—and outlines the benefits for end users, institutions, and the community. Additionally, it highlights the need for trust among institutions and suggests starting with unique and less controversial entities for effective reconciliation.

1. STANFORD UNIVERSITY LIBRARIES
The Linked Data Snowball or
Why We Need Reconciliation
April 4th, 2016
TH E AAC / G E T T Y WO R K S H O P O N
R E C O N C I L I AT I O N O F L I N K E D OP E N D ATA
Rob Sanderson / azaroth@stanford.edu / @azaroth42

2. STANFORD UNIVERSITY LIBRARIES
The Linked Data Snowball or
Why We Need Reconciliation
April 4th, 2016
TH E AAC / G E T T Y WO R K S H O P O N
R E C O N C I L I AT I O N O F L I N K E D OP E N D ATA
Rob Sanderson / azaroth@stanford.edu / @azaroth42
web.stanford.edu/~azaroth/#me
azaroth42@gmail.com / +azaroth42
orcid: 0000-0003-4441-6852

3. STANFORD UNIVERSITY LIBRARIES
The Linked Data Snowball or
Why We Need Reconciliation
April 4th, 2016
T H E A A C / G E T T Y W O R K S H O P O N
Rob Sanderson / azaroth@stanford.edu / @azaroth42
web.stanford.edu/~azaroth/#me
azaroth42@gmail.com / +azaroth42
orcid: 0000-0003-4441-6852
http://www.informatik.uni-trier.de/~ley/pers/hd/s/Sanderson:Robert
http://academic.research.microsoft.com/Author/2765999
http://www.scopus.com/authid/detail.url?authorId=8988953600
www.researchgate.net/profile/Rob_Sanderson
facebook.com/rob.sanderson / linkedin.com/pub/robert-sanderson/1/172/5a6/
rsanderson@lanl.gov / azaroth@liv.ac.uk
public.lanl.gov/rsanderson / gondolin.hist.liv.ac.uk/~azaroth
rds23@student.canterbury.ac.nz / azaroth@es-net.co.nz
R E C O N C I L I AT I O N O F L I N K E D O P E N D ATA

4. Linked Data?
1. Use URIs as names for things
2. Use HTTP URIs so that people can look up those names
3. When someone looks up a URI, provide useful
information, using the standards
4. Include links to other URIs, so they can discover
more things

5. Linked Data?
1. Use URIs as names for things
2. Use HTTP URIs so that people can look up those names
3. When someone looks up a URI, provide useful
information, using the standards
4. Include links to other URIs, so they can discover more
things
5. Link your data to other people's data to provide
context

7. Why So Many?
Do I know the URI, or can I find it?
URI
No

8. Why So Many?
Do I know the URI, or can I find it?
No
Understand and agree with the model used?
No
URI

9. Why So Many?
Do I know the URI, or can I find it?
No
Understand and agree with the model used?
No
Understand and agree with the description?
No
URI

10. Why So Many?
Do I know the URI, or can I find it?
No
Understand and agree with the model used?
No
Understand and agree with the description?
No
Agree the URI identifies the same entity?
No
URI

11. Why So Many?
Do I know the URI, or can I find it?
No
Understand and agree with the model used?
No
Understand and agree with the description?
No
Agree the URI identifies the same entity?
No
Agree description is complete?
No
URI

12. Why So Many?
Do I know the URI, or can I find it?
No
Understand and agree with the model used?
No
Understand and agree with the description?
No
Agree the URI identifies the same entity?
No
Agree description is complete?
No
Hooray, you reused a URI! URI
Yes

13. Why So Many?
Do I know the URI, or can I find it?
No
Understand and agree with the model used?
No
Understand and agree with the description?
No
Agree the URI identifies the same entity?
No
Agree description is complete?
No
Hooray, you reused a URI!
Now start again with the next entity :(
URI
Yes

14. Many Special and Unique Snowflakes

15. Become a Huge Snowball of Technical Debt

16. Option 1: Balance the Equation
Cost(Create URI)!
+!
Cost(Maintain URI) !
!
Cost(Find Good URI)+
Cost(Understand Model)+
Cost(Understand Content)
+!
min( Risk(Reliability)+!
Cost(Network Latency),!
Risk(Out of Date)+!
Cost(Cache Content))
-!
Value(Connected Graph)!
<=

17. Option 1 Likelihood

18. Option 1 Likelihood
Botticelli: http://vocab.getty.edu/ulan/500015254!

19. Option 1 Likelihood
Botticelli: http://vocab.getty.edu/ulan/500015254 :)!

20. Option 1 Likelihood
Botticelli: http://vocab.getty.edu/ulan/500015254!
:(

21. Option 2: Reconciliation
YCBA's URIs Princeton's URIs

22. Option 2: Reconciliation
YCBA's
Entities
Princeton's
Entities
Shared Entities but not Shared URIs

23. Option 2: Reconciliation
1. Algorithmically discover this intersection
given the descriptions of the entities

24. Option 2: Reconciliation
2. Assert that the entity which two URIs identify
is actually the same entity
=

25. Option 2: Reconciliation

26. Option 2a: Reconciliation
(distributed authority)

27. Option 2b: Reconciliation
(centralized authority)

28. Benefits of Reconciliation
End User:
• Has access to more information, more easily, improving research,
discovery and navigation
• Potential for new UIs, new research questions, reasoning
Institution:
• Efficiency (= reduced cost) and improved quality of description
• Increased prestige when descriptions are reused
• Usage across the network is valuable business intelligence
Community:
• Network effects spread faster and further, increasing awareness of
cultural heritage
• Gives easier access to other communities' data

29. Real Benefit of Reconciliation
Reconciliation is a network damage limiting step
towards balancing Equation 1
By linking entity descriptions together:
• the cost of discovery and understanding is reduced
• the costs of creating and maintaining the resources are shared
across the community, not duplicated
• the value of the connected graph is increased
• the likelihood of new entities (requiring reconciliation) is reduced

30. But How Can A Machine Know??
Algorithms won't be perfect, but can be good enough.
• What use cases will the reconciled data be used to fulfill?
• What is the cost of a false positive for those use cases?
Precision: What % of matches are correct?
Recall: What % of the possible matches were found?
Can make trade-offs of precision vs recall for different use cases.
Machine can record its certainty, and policy can provide a threshold.

31. How Can We Improve It?
Several different relationships to express similarity:
• owl:sameAs – always exactly the same (transitive)
• skos:exactMatch – the same for most purposes (transitive)
• skos:closeMatch – the same for some purposes (intransitive)
The context of resource in the network is important
• Starting simple with high precision gives a better context to use the
results to iteratively and incrementally bootstrap

32. Trust and Community
"Efficiency (= reduced cost) and improved quality of description"
• Efficiency comes from not duplicating descriptive effort...
• Which requires trusting other institutions in the community
• We need to work together, not...

33. Trust and Community
"Efficiency (= reduced cost) and improved quality of description"
• Efficiency comes from not duplicating descriptive effort...
• Which requires trusting other institutions in the community
• We need to work together, not...

34. Entities to Reconcile
As a community, we need to pick where to start.
Suggest starting with least controversial / most unique:
• Physical objects
• People
• Places
• Events (specific, like Exhibitions)
A small sub-domain (by time?) to make overlap more likely

35. Q. Can I Reconcile a String?
Named Entity Recognition
"snowflake" = .
strings to things
Reconciliation
. = .
things to things

36. The Hard Question
How can we be more useful than DBPedia
for our own entities?

37. The Hard Question
How can we be more useful than DBPedia
for our own entities?
• Focus on unique selling points
• Demonstrate value early,
both internally and to the broader community
• By working together to increase the value of the network

38. STANFORD UNIVERSITY LIBRARIES
Thank You!
April 4th, 2016
Rob Sanderson / azaroth@stanford.edu / @azaroth42
web.stanford.edu/~azaroth/#me
azaroth42@gmail.com / +azaroth42
orcid: 0000-0003-4441-6852
http://www.informatik.uni-trier.de/~ley/pers/hd/s/Sanderson:Robert
http://academic.research.microsoft.com/Author/2765999
http://www.scopus.com/authid/detail.url?authorId=8988953600
www.researchgate.net/profile/Rob_Sanderson
facebook.com/rob.sanderson / linkedin.com/pub/robert-sanderson/1/172/5a6/
rsanderson@lanl.gov / azaroth@liv.ac.uk
public.lanl.gov/rsanderson / gondolin.hist.liv.ac.uk/~azaroth
rds23@student.canterbury.ac.nz / azaroth@es-net.co.nz

39. STANFORD UNIVERSITY LIBRARIES
Thank You!
April 4th, 2016
Rob Sanderson / azaroth@stanford.edu / @azaroth42
web.stanford.edu/~azaroth/#me
azaroth42@gmail.com / +azaroth42
orcid: 0000-0003-4441-6852
http://www.informatik.uni-trier.de/~ley/pers/hd/s/Sanderson:Robert
http://academic.research.microsoft.com/Author/2765999
http://www.scopus.com/authid/detail.url?authorId=8988953600
www.researchgate.net/profile/Rob_Sanderson
facebook.com/rob.sanderson / linkedin.com/pub/robert-sanderson/1/172/5a6/
rsanderson@lanl.gov / azaroth@liv.ac.uk
public.lanl.gov/rsanderson / gondolin.hist.liv.ac.uk/~azaroth
rds23@student.canterbury.ac.nz / azaroth@es-net.co.nz

40. STANFORD UNIVERSITY LIBRARIES
Thank You!
April 4th, 2016
azaroth@stanford.edu

41. STANFORD UNIVERSITY LIBRARIES
Thank You!
April 4th, 2016
azaroth@stanford.edu

42. Thank You!
rsanderson@ge*y.edu
April 25th, 2016

43. Thank You!
rsanderson@ge*y.edu
Based on my slides from Andrew W. Mellon Foundation Reconciliation Workshop
With recognition and thanks to all of the participants