Agricultural Data Interest Group (IGAD) Pre-Meeting Agenda Research Data Alliance 21st to 22nd September 2015 INRA, Paris (France)

1. Ontology-­‐based
services
and
knowledge
management
in
the
Agronomic
Domain
Pierre
Larmande
Ins-tute
of
Research
for
Development
(IRD)
Head
of
data
integra-on
group
at
the
Ins-tute
of
Computa-onal
Biology
pierre.larmande@ird.fr

2. Outline
• Data integration challenges in the Life Sciences
• Ontologies/ Semantic Web Technologies
• AgroPortal a proposition for ontology-based services in the
agronomic domain
• Agronomic Linked Data project

3. Data landscape in the Life Sciences
• The availability of biological data has increased
• Advancements in:
• computational biology
• genome sequencing
• high-throughput technologies
• Integrative approaches are necessary to understand
the functioning of biological systems

4. • Lack of effective approaches to integrate data that
has created a gap between data and knowledge
• Need for an effective method to bridge gap between
data and underlying meaning
• Harvest the power of
overlaying different data sets
Data integration challenges

5. Semantic Web Technology
• An extension of the current Web technologies.
• Enables navigation and meaningful use of digital
resources.
• Support aggregation and integration of information
from diverse sources.
• Based on common and standard formats.

6. Resource Description Framework (RDF)
• Framework for representing information about resources
on the Web
• Provides a labeled connection between two resources
• Uses Unique Resource Identifiers (URI)
• Statements take the form of triples:
Subject
Predicate
Object
<Gene_A>
<codes_for>
<Protein_A>
RDF
Triple

7. • Combining the triples results in a directed, labeled
graph.
<Gene_A>
<Protein_A>
<has_funcFon>
<BP_A>
<MF_A>
<Gene_X>
<regulates>
7

8. AgroPortal
a
proposi(on
for
ontology-­‐based
services
in
the
agronomic
domain
Clément
Jonquet,
Esther
Dzalé-­‐Yeumo,
Elizabeth
Arnaud,
Pierre
Larmande

9. ObjecFves
of
AgroPortal
project
• Develop
and
support
a
reference
ontology
repository
for
the
agronomic
domain
– One-­‐stop-­‐shop
for
plant/agronomic
related
ontologies
– Primary
focus
on
the
agronomic
&
plant
domain
• Reusing
the
NCBO
BioPortal
technology
– Avoid
to
re-­‐implement
what
has
been
done
– Facilitate
interoperability
– Reusing
the
scien-fic
outcomes,
experience
&
methods
of
the
biomedical
domain
• Enable
straighUorward
use
of
agronomic
related
ontologies
– Respect
the
requirements
of
the
agronomic
community
– Fully
seman-c
web
compliant
infrastructure
9

10. HOW
DOES
IT
LOOKS?
10

11. 11

12. 12

13. Available
ontologies
• Already
29
ontologies…
and
we
expect
around
40
soon.
– (half
are
not
included
in
the
NCBO
BioPortal)
• Ontologies
are
organized
in
Groups
and
Categories
13

14. 14

15. 15

16. Recommender
16

17. Mappings
17

18. Community
based
func-onali-es
Atelier
InOvive
2015
–
Rennes
–
29
juin
2015
18

19. REST
Web
Service
API:
hhp://data.agroportal.lirmm.fr/documenta-on
Atelier
InOvive
2015
–
Rennes
–
29
juin
2015
19

20. SPARQL
endpoint:
hhp://sparql.agroportal.lirmm.fr
20

21. AN
ONTOLOGY
REPOSITORY…
WHO’S
GONNA
USE
IT?
21

22. 4
Driving
Agronomic
Use
Cases
• IBC
Rice
Genomics
– data
integra-on
and
knowledge
management
related
to
rice
• RDA
Wheat
Data
Interoperability
working
group
– common
framework
for
describing,
represen-ng,
linking
and
publishing
wheat
data
with
respect
to
open
standards
• INRA
Linked
Open
Vocabularies,
LovInra
– publish
vocabularies
produced
or
co-­‐produced
by
INRA
scien-sts
and
foster
their
reuse
beyond
the
original
researchers
• The
Crop
Ontology
project
– publishes
ontologies
required
for
describing
crop
germplasm,
traits
and
evalua-on
trials.
22

23. Each
use
case
has
a
specific
group
in
AgroPortal
• Feature
to
come:
slices
– Specific
“entry”
in
the
AgroPortal
23

24. AgroLD
The
Agronomic Linked
Data project
Aravind
Venkatensan,
Gildas
Tagny,
Nordine
El
Hassouni,
Manuel
Ruiz,
Pierre
Larmande

25. Agronomic Linked Data (AgroLD)
• Semantic web based system that integrates data from
South Green Bioinformatics node
• Aim:
• Capability to answer complex real life questions
• Efficient information integration / retrieval.
• Easy extensibility.
• Aid in holistic understanding of domain

26. AgroLD
• AgroLD will be developed in phases –
• Website: www.agrold.org
• Phase I: includes data on:
• Rice (Oryza spp).
• Oryza barthi
• Oryza brachyantha
• Oryza Sativa
• Oryza glaberimma
• Arabidopsis thaliana
• Sorghum (Sorghum bicolor)
• Maize/Corn (Zea mays)
• Wheat
• Triticum astivum
• Triticum urartu

27. Data
sources
in
AgroLD

28. Ontologies
in
AgroLD

29. Knowledge in AgroLD
AgroLD
Ontologies

30. www.agrold.org

31. Search
and
browse
AgroLD
Plant
height

32. Sparql
query
editor

33. Sparql
query
editor

34. Results
are
annotated
with
evidence_code
hhp://geneontology.org/page/guide-­‐go-­‐evidence-­‐codes

35. VisualisaFon
of
queries

36. Advanced
form-­‐based
search

37. Results
are
combined
with
external
services

38. Please
send
us
your
Feedback!
Your
answers
will
help
us
to
improve
the
applicaton

39. Acknowledgements
Elizabeth
Arnaud,
Leo
Valee,
Marie-­‐Angelique
Laporte,
Julian
Pietragalla
Manuel
Ruiz,
Nordine
El
Hassouni
Aravind
Venkatesan,
Gildas
Tagny
Esther
Dzalé-­‐Yeumo,
Cyril
Pommier
Patrick
Valduriez
Clement
Jonquet
Pierre
Larmande
Contact:
pierre.larmande@ird.fr