Linked Data:Principles and Practice

Joe FutrelleWoods Hole Oceanographic Institution

jfutrelle@whoi.edu

WHOI / BCO-DMO, July 11, 2011

Grand challenge: whole systems

Observation and modelling of multiple systems at multiple scalesLinking data from different disciplinesto get useful global results!

“... modelling complex systems will be a major research challenge for the 21st century”- National Science Foundation

Building current practices up isn't working

Heterogeneous tools, data formatsCan’t get everyone in one workgroupFunding goes to science, not stewardship

M.C. Escher, “Tower of Babel” (1928)

Proposed solutions aren't working

• e-Journals – not machine-interpretable• Collaboration tools

– everyone falls back on email & other p2p• Portals and repositories – typically:

– centralized– domain-specific

• “The Grid” – can orchestrate complex processing jobs, but that's not science

Only networks work at scale

Single researcherAd hoc data mgt, single-user appsCommunityCommunity tools, resources, controlGlobalNo global practice, tools, control

Desktop

Workgroup

Network

Or to put it another way …

Ted Nelson, Computer Lib / Dream Machines (1974)

Data is the network

There is no boundary, center, or locus of control,… so it scales

linkeddata.org (2009)

Benjamin Franklin (1754)

“If you can’t tweet your dataset, it doesn’t exist”

• Links are the global currency of the internet

• The more people link to you, the more you matter (e.g., Page rank)

• If nobody can link to your data, they will choose data they can link to instead

• If someone links to your data, someone will link to them, and thus to you

• The lowest entry barrier wins

Don’t drink the Kool-aid• Semantic web

“layer cake”• Where do we do

actual work?– User interface?– Applications?

• “Semantic Grid” (D. DeRoure, C. Goble)

(source: World Wide Web Consortium)

Semantics = what they hear• Shared semantics

are minimal• Maximal

semantics emerge when multiple nodes act on partial information

• Validating each exchange doesn’t scale

Gary Larson (1983)

Design data for network effects• Global, persistent identification• Open models (tolerate incompleteness)• Transparent protocols (pass-through)• “Graceful degradation” (cf. Dublin Core)• Data outlives code, so data should

control code, not the other way around• Semantics matter, so they must be

explicit and machine-readable (not a side effect of running code)

Practices that grow the network• Give everything a portable identifier• Link entities via properties = network• Reuse existing ontologies and only build

the partial ontologies that fill in the gaps (e.g., don’t re-develop Dublin Core terms)

• Emit metadata early and often; don’t assume curators will do it later (who? $?)

• “Not building a wall; building a brick” (Oblique Strategies, 1970)