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Linked Data: Principles and Practice. Joe Futrelle Woods Hole Oceanographic Institution jfutrelle@whoi.edu WHOI / BCO-DMO, July 11, 2011. Grand challenge: whole systems. Observation and modelling of multiple systems at multiple scales Linking data from different disciplines

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Linked data principles and practice

Linked Data:Principles and Practice

Joe FutrelleWoods Hole Oceanographic Institutionjfutrelle@whoi.edu

WHOI / BCO-DMO, July 11, 2011


Grand challenge whole systems
Grand challenge: whole systems

  • Observation and modelling of multiple systems at multiple scales

  • Linking data from different disciplines

  • to 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
Building current practices up isn't working

  • Heterogeneous tools, data formats

  • Can’t get everyone in one workgroup

  • Funding goes to science, not stewardship

M.C. Escher, “Tower of Babel” (1928)‏


Proposed solutions aren t working
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
Only networks work at scale

  • Single researcher

    • Ad hoc data mgt, single-user apps

  • Community

    • Community tools, resources, control

  • Global

    • No global practice, tools, control

Desktop

Workgroup

Network


Or to put it another way
Or to put it another way …

Ted Nelson, Computer Lib / Dream Machines (1974)


Data is the network
Data is the network

linkeddata.org (2009)

There is no boundary, center, or locus of control,

… so it scales



If you can t tweet your dataset it doesn t exist
“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
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
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
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
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)