# Developing semantic interoperability in ecology and ecosystem studies

Christian Pichot, Cécile Callou, Andre Chanzy, Philippe Clastre, Chloé

Martin, Damien Maurice, Ghislaine Monet

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► To cite this version:

Christian Pichot, Cécile Callou, Andre Chanzy, Philippe Clastre, Chloé Martin, et al.. Developing semantic interoperability in ecology and ecosystem studies. RDA 17th Plenary Meeting, Apr 2021, Edinburgh, United Kingdom. . [hal-03234155](https://hal.inrae.fr/hal-03234155)

HAL Id: hal-03234155

https://hal.inrae.fr/hal-03234155

Submitted on 25 May 2021

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# Developing semantic interoperability in ecology and ecosystem studies

PICHOT Christian¹, CALLOU Cécile², CHANZY André³, CLASTRE Philippe¹, MARTIN Chloé², MAURICE Damien⁴, MONET Ghislaine⁵

1. INRAE URFM 228 route de l'Aérodrome 84914 Avignon

2. CNRS UMS BBEES 55 rue Buffon 75005 Paris

3. INRA UMR EMMAH 228 route de l'Aérodrome 84914 Avignon

4. INRAE UMR SILVA route d'Amance 54280 Champenoux

5. INRA UMR CARRTEL 75 avenue de Corzent 74200 Thonon-les-Bains

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## Rationale

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Ecosystem study requires complex research and deals with heterogeneous, varied and widespread data.

The proper understanding and interoperability of the information sources remains one of the greatest challenges
Method

1) Identify

- the components of the system

- and their relationships

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Ht: 17.7 m

dbh: 520 mm

age: 35 y

depth: 70 cm

swr: 80 mm

2) Model the system

using semantic vocabularies

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Implementation

**AnaEE* RI as scientific context:**
The Research Infrastructure offers services
for experimentation on continental ecosystems

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**OBOE* as ontological framework:**
The ontology provides the atomic
elements for modeling observations

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*Mark Schildhauer, Matthew B. Jones, Shawn Bowers, Joshua Madin, Sergeui Krivov, Deana Pennington, Ferdinando Villa, Benjamin Leinfelder, Christopher Jones, and Margaret O'Brien. 2016. OBOE: the Extensible Observation Ontology, version 1.2. KNB Data Repository. doi:10.5063/F1125R0F
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ENVRI
FAIR

Implementation

Modeling the experimental context

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Implementation

Modeling the measured variables

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## Implementation

### Graph pattern approach:

Design parametric graphs suitable for a set of variables

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Semantic lifting and data exploitation

Graph patterns and variable semantic descriptions are processed by a pipeline
for semantic lifting of the data before their exploitation

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Lessons from this work

* The OBOE generic 'observation model' allows for efficient atomic modeling of the components of the system and of their nested or crossed relationships.

* In addition to the provided OBOE extensions (characteristics, spatial, temporal, standards), the unique identification of the system components is ensured through new classes and individuals especially for Entity (e.g experimental entities) and Standards (e.g lists of variable names or of experimental facilities).

* A graph pattern approach for the modeling of the variables leads to a more efficient investment at greatly reduced cost

* When data are initially managed in structure databases, the data modeling is directly exploitable by pipelines for mass lifting to rdf graphs

* The whole process produces syntactically and semantically interoperable data, contributing to FAIR sharing and data reuse