A faceted search system for facilitating discovery-driven scientific activities: a use case from functional ecology

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Laporte, Marie-Angélique | Garnier, Éric | Mougenot, Isabelle

Edité par CCSD -

International audience. To address biodiversity issues in ecology and assess the consequences of ecosystem changes, large quantities of long-term observational data from multiple data sets need to be integrated and characterized in a unified way. During these last decades, functional trait-based approaches have shown great potential to facilitate the understanding and the prediction of ecosystem changes. To promote data exchange, portability and to drive higher communiction between systems, scientific communities are required to acquire data standards. Semantic web (or web of data) provides a realistic solution for these exact requirements. Consequently semantic web allows for creative approaches and offers opportunities to scientists to gain new insight from experimental data. A first step to this goal is to standardize meaningful and precise terms that are interlinked through a dedicated thesaurus that covers the plant functional diversity domain. Therefore this vocabulary can serve as stable reference resources for integration purposes, specifically when published in RDF language and available as linked data on the web. This manuscript presents a web infrastructure, named Thesauform, that fully exploits the key principles of the web of data and its common open data structures in order to guide the plant functional diversity community of experts to build collectively, manage, visualize and query a SKOS thesaurus. A thesaurus dedicated to plant functional traits is used to demonstrate the potential of the approach. Indeed, the thesaurus, built using the Thesauform tool, is used to semantically annotate heterogeneous data sources, such as the TRY database or the Plant Ontology. Then, a faceted search system, based on SKOS collections, enabling thesaurus browsing according to each end-users requirements is expected to greatly enhance the data discovery in the context of biodiversity studies.

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