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COMMIT/
THE SEMANTIC WEB AS A SCIENCE ACCELERATOR
Frank van Harmelen
Water, Water Everywhereand not a drop to drinkRime of the Ancient MarinerSamuel Taylor Coleridge, 1797
Data, Data Everywhereand not a thought to thinkAverage Scientist, 2013
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THE SCIENTIST’S PROBLEM
Too much unintegrated data: from a variety of incompatible
sources no standard naming convention each with a custom browsing and
querying mechanism (no common interface)
poor interaction with other data sources
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WHAT ARE THE DATA SOURCES?
Flat FilesURLsProprietary DatabasesPublic DatabasesSpreadsheetsEmails…
5
Not just the Big Sciences• Archeology• Chemistry• Genomics, proteomics, ... (bio/life-sciences)• Communication science• Social history• Linguistics• Bio-diversity• Environmental sciences (climate studies)• ....• libraries (KB), archives (beeld&geluid)
One dataset per sitea new database each month
historical datalaymen data
international data
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"WEB OF DATA" (TBL)
recipe:expose databases on the web, use RDF, integrate
meta-data from:•expressing DB schema semantics in machine interpretable ways
enable integration and unexpected re-use
P1. Give all things a nameCOMMIT/
P2. Relations form a graph between things
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P3. The names are addresses on the Web
x T
[<x> IsOfType <T>]
differentowners & locations
<analgesic>
P1+P2+P3 = Giant Global GraphCOMMIT/
P4. explicit & formal semantics
• assign types to things• assign types to relations• organise types in a hierarchy• empose constraints on
possible interpretations
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Examples of “semantics”
Semantics = predictable inference
Frank Lyndabirth-place
• Frank is person• birth-place relates
person to location
• birth-place relates 1 person to 1 location
• Lynda = Hazellowerbound upperbound
Hazelbirth-place
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> 25 billion sta
tements
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TWO EXAMPLES
Hubble
Linkitup
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EXAMPLE 2
Hubble
Linkitup
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DON’T JUST PUBLISH YOUR DATA
Turn your data into URLsExpose these on the WebLink them with existing
vocabularies
Better use for youBetter re-use for others
