Interlinked data and models using a semantic approach: example of the RECORD platform in the context of the ANAEE-France project

Transcription

1 Interlinked data and models using a semantic approach: example of the RECORD platform in the context of the ANAEE-France project H.Raynal (U.MiAT, INRA, Toulouse), A.Chanzy (UMR EMMAH, INRA, Avignon), F. Lafolie (UMR EMMAH, INRA, Avignon), M. El Hadramy (U.MiAT, INRA, Toulouse), D.Maurice ( INRA, Nancy), E. Casellas (U.MiAT, INRA, Toulouse) NOM DE L AUTEUR AgMIP Montpellier 2016 JOUR / MOIS / ANNEE

2 Outline Context Ontology a tool for interoperability Different steps.02

3 Context

4 ANAEE-France Analysis and Experimentation on Ecosystems - France Ongoing project (INRA & CNRS) Aims to develop a national research infrastructure for the study of continental ecosystems (including agrosystem) and their biodiversity. This infrastructure brings together modelling platforms and databases for long term experiments Two challenges: To ease access to and sharing of models and experimental data To develop interoperability between the different numerical elements of the infrastructure: databases and models A lot of tasks in this project. Here is a focus on: Improving interoperability between models (on RECORD platform) and databases using the approach of linked data. Linked data a term used to describe a recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF. (Source Wikipedia).04 JOUR / MOIS / ANNEE

5 Why developping «generic» interoperability? Modelling platform A library of models with different inputs/outputs (name of variable, units) Model typically needs to attach to external sources of data (e.g weather observations, soil characteristics, management practices ) Database Different sets of experimental data on agro-ecosystem Protocol description Observed data.05 JOUR / MOIS / ANNEE

6 Why developping «generic» interoperability? not a «one shot interoperability» Modelling platform A library of models with different inputs/outputs (name of variable, units) Model typically needs to attach to external sources of data (e.g weather observations, soil characteristics, management practices ) Database Different sets of experimental data on agro-ecosystem Protocol description Observed data Ex. study of the evolution of soil carbon in forest Modelling platform Yasso model (Jari Liski Finnish Environement Institute of Helsinki, 2009 ) SOERE FORET Database Long term experiment in forest.06 JOUR / MOIS / ANNEE

7 Example of use: design of web application based on this interoperability C stock observed (green triangles) simulated (blue line) - future.07

8 Ontology: a tool for interoperability OBOE

9 Ontology A commonly used tool that helps in solving interoperability problems Interoperability: capacity of software components to exchange: informations Data Objects. Examples: AGROVOC (FAO) thesaurus to exchange informations SANDRE (French administration for water) Schemadata + language to exchange data on water Ontology: A major tool of semantic web Community Semantic High level of formalization (graph approach) F. Villa et al. / Environmental Modelling & Software 24 (2009) NOM DE L AUTEUR / NOM DE LA PRESENTATION.09 JOUR / MOIS / ANNEE

10 What bring us to the choice of OBOE? No existing ontology (even by combining different ontologies) to cover all the domain covered by ANAEE-France Domain of application «ecosystem» : wide field decided to choose as starting point a generic ontology, and to applied it (rather than an ontology that describes explicitly the domain) OBOE is used for modeling and representing scientific observations that helps in designing frameworks or applications (mainly in the domain of ecological data) Analogy can be made between simulation and field experiment, so OBOE can also be used in the context of models..010

11 OBOE ontology a simple pattern: 5 core classes and 7 properties OBOE conceptual model (Madina et al. 2007).011 JOUR / MOIS / ANNEE

12 Example of use of OBOE-ANAEE, in the context of model & data applied to Carbon in soil forest the same conceptual schema is applied to describe the observed variable and to describe simulated output variable (same approach for input variables and characteristics of experimental protocol).012

13 OBOE and ICASA Variables International Consortium for Agricultural Systems Applications (ICASA) a naming convention for agricultural model variables. ( AgMIP standards in the harmonized AgMIP Crop Experiment (ACE) database. Name (ICASA name) Context level 2 Context level 1 Entity Characteristic Unit canopy_height crop vegetation cover canopy height leaf_carbohydrate crop leaf carbohydrate concentration grain_carbohydrate_conc grain carbohydrate concentration growth_stage crop growth stage root_weight/length crop vegetation cover root mass per length g cm-1 growth_respiration_daily crop vegetation cover respiration mass per surface g m-2 day-1 dead_canopy_dry_wt crop vegetation cover dead vegetation dry mass per surface kg ha-1 grain_dry_weight crop vegetation cover grain dry mass per surface kg ha-2 total_biomass_dry_wt crop vegetation cover total vegetation dry mass per surface kg ha-3 root_depth crop vegetation cover root length m leaf_area_index crop vegetation cover leaf surface by surface m2.m-2 stem_area_index crop vegetation cover stem surface per surface m2.m-2 incident shortwave Atm. radiation surfacic energy global hourly atmosphere radiation surfacic energy MJ.m-2 atmosphere air temperature maximum at 10 atmosphere air at 10 cm height temperature K NH4_Nsoil_total soil ammonium nitrogen mass per surface kg ha-1 NO3_Nsoil_total soil nitrate nitrogen mass per surface kg ha-1 sowing_date sowing date string ploughing_date ploughing date string sowing_density sowing mass per surface kg ha-1 ploughing_depth ploughing depth m.013 JOUR / MOIS / ANNEE

14 Different steps

15 Semantic web: An extension of the web (W3C) with standards that promote common data formats and exchange protocol on the web (Just to keep in mind ) the main underlying concepts and technologies: - RDF a general method for describing informations RDF schema - SPARQL ardf query language - OWL Web Ontology Language NOM DE L AUTEUR / NOM DE LA PRESENTATION.015 JOUR / MOIS / ANNEE

16 Different steps 1rst step: ontology building OBOE OBOEE-ANAEE (define all the entities, the characteristics, the relationships ). Online version of the ontology shared by the community 2 nd step : annotation process Annotate all the inputs and outputs of each model of RECORD Annotate all the sets of data of the databases of the infrastructure (in order to have a file describing the inputs, outputs or observations, compliant to the ontology and in RDF format) 3 rd step: ready for designing application which require data and models. using: Distributed architecture. Request on the different elements of the infrastructure Exchange (webservices) NOM DE L AUTEUR / NOM DE LA PRESENTATION.016 JOUR / MOIS / ANNEE

17 Example of use: design of web application based on this interoperability C stock observed (green triangles) simulated (blue line) - future.017

18 Conclusion Very short introduction to how web semantic can help modellers in the straightforward task : linking database and model. A focus on ontology, and OBOE ontology Application to ANAEE-France infrastructure And now Ensemble modelling a big work to link models and data. Ontology, websemantic A new perspective/pilot for AGMIP community ensemble modelling (and in particular AgMIP work)?.018

19 Thank you