Linked Data Snowball, or Why We Need Reconciliation

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 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

Rob Sanderson / [email protected] / @azaroth42



April 4th, 2016

T H E A A C / G E T T Y W O 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 O P E N D ATA

Rob Sanderson / [email protected] / @azaroth42 web.stanford.edu/~azaroth/#me

[email protected] / +azaroth42 orcid: 0000-0003-4441-6852



April 4th, 2016

T H E A A C / G E T T Y W O R K S H O P O N



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/ [email protected] / [email protected]

public.lanl.gov/rsanderson / gondolin.hist.liv.ac.uk/~azaroth [email protected] / [email protected]

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

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

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

Why So Many? Do I know the URI, or can I find it?

URI

No


No

Understand and agree with the model used? No

URI


No


Understand and agree with the description? No

URI


No



Agree the URI identifies the same entity? No

URI


No




Agree description is complete? No

URI


No





Hooray, you reused a URI! URI Yes


No





Hooray, you reused a URI! Now start again with the next entity :(

URI Yes

Many Special and Unique Snowflakes

Become a Huge Snowball of Technical Debt

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Cost(Network Latency),!Risk(Out of Date)+!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)!

<=

Option 1 Likelihood

Option 1 Likelihood

Botticelli: http://vocab.getty.edu/ulan/500015254!

Option 1 Likelihood

Botticelli: http://vocab.getty.edu/ulan/500015254 :)!

Option 1 Likelihood

Botticelli: http://vocab.getty.edu/ulan/500015254!:(

Option 2: Reconciliation

YCBA's URIs Princeton's URIs


YCBA's Entities

Princeton's Entities

Shared Entities but not Shared URIs


1. Algorithmically discover this intersection given the descriptions of the entities


2. Assert that the entity which two URIs identify is actually the same entity

=


Option 2a: Reconciliation (distributed authority)

Option 2b: Reconciliation (centralized authority)

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

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

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.

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

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...

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...

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

Q. Can I Reconcile a String?

Named Entity Recognition

"snowflake" = .

strings to things

Reconciliation

. = .

things to things

The Hard Question

How can we be more useful than DBPedia for our own entities?

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


Thank You!

April 4th, 2016








Thank You!

April 4th, 2016








Thank You!

April 4th, 2016

[email protected]


Thank You!

April 4th, 2016

[email protected]

ThankYou!

rsanderson@ge*y.edu

April 25th, 2016

ThankYou!

rsanderson@ge*y.edu

Based on my slides from Andrew W. Mellon Foundation Reconciliation Workshop With recognition and thanks to all of the participants

Technology

Linked Data Snowball, or Why We Need Reconciliation