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Tdwg2011
1.
Semantic extensions to
ecoRelevé Olivier Rovellotti
2.
• The projet •
The Sem Web • The extentions
3.
Reduce Software complexity
4.
For who? People we
know: People we don’t know?
5.
What for?
Ask Analyze Questions data Define Data Admin Protocols Collect data
6.
DBA/GIS
Research Professional Citizen Science PHD 3 Modules
8.
RDF
9.
2007
10.
2009
11.
2012 Life Science
12.
Now what ?
13.
What is it
good for? • Better annotations • Easier data integration • More extensible • More expressive
14.
Aggregate data
Controlled Vocabularies: Enhance existing Build protocols dataset forms
15.
Annotations
16.
ecoRelevé Core
18.
1.0 Data integration
Observation Layers
19.
Data enhancement
ecoRelevé explorer
21.
4 dimensions
Space People Biology Time
22.
GeoSparql
Localities Protected Areas Work Conservation Status Space FOAF Time People Biology Friends Family At the same time as Last week
23.
Data connector
24.
Milan royal Milvus milvus Red
Kite
25.
Rod Page -
what can you do with it ?
26.
SPARQL: Catch the frog
TAISTY! select ?scientificName, ?status, ?lat, ?long ecoReleve FROM <urn:rdf.TdwgFroggyChallenge> where { ?s rdf:type uniprot:Molecule . ?s terms:relation ?id. ?s terms:subject ?taxonInGB . SparQL ?id geo:lat ?lat. ?id geo:long ?long. ?taxonInDbPedia dbOwl:conservationStatus ?status. ?taxonInGB rdfs:seeAlso ?taxonInDbPedia. ?taxonInGB uniprot:scientificName ?scientificName. }
28.
Take home message
: • There is a learning curve • But RDF is not that difficult • One API is better than 10 • We need data in RDF to experiment • Reasoning is for later …
29.
Thanks .. @orovellotti
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