This document discusses semantic extensions that were made to the ecoRelevé software to represent observational biodiversity data using semantic web technologies like RDF. It describes adding RDF representations for observations, localities, protected areas, and other core ecoRelevé concepts. This allows for enhanced data integration, aggregation, and exploration through semantic queries using SPARQL. The extensions aim to reduce complexity, make the data more expressive and extensible, and help integrate existing datasets through controlled vocabularies and annotations. Examples are provided of SPARQL queries that can be performed on the RDFized ecoRelevé data.

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