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Linked Data: Principles and Practice. Joe Futrelle Woods Hole Oceanographic Institution [email protected] 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 [email protected]

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)
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