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Rapport Année : 2024

A Unified Approach to Publish Semantic Annotations of Agricultural Documents as Knowledge Graphs

Résumé

This paper presents a generic semantic model to describe, structure and integrate the named entities automatically extracted from texts coded as annotations. This model has been used to construct three different knowledge graphs from three distinct agricultural corpora in two different languages (English and French). The two first corpora consist of PubMed scientific publications, written in English, on wheat and rice genetics and phenotyping. The named entities to be recognised are genes, phenotypes, traits, genetic markers, and taxa. Those entities are normalized using domain semantic resources such as the Wheat Trait and Phenotype Ontology (WTO). The third corpus contains agricultural alert bulletins published in France and written in French. The named entities to be recognised are crop names and phenological development stages. Crop names are defined in the French Crop Usage (FCU) thesaurus while development stages are formalized using the BBCH-based Plant Phenological Description Ontology (PPDO). For the three corpora, named entities were automatically extracted using natural language processing tools. We present an approach that relies on the formalization of a semantic data model based on common and well-adopted linked open vocabularies such as Web Annotation Ontology (OA) and Provenance ontology (PROV). The model describes the named entities and their links to vocabularies. It was slightly adapted to each corpus annotations. The model was populated using a mapping-based transformation pipeline implemented with the Morph-xR2RML tool which takes CSV files as input . The development of the proposed model was initiated by the formulation of motivating scenarios by experts in the domain of each corpus that led to a set of competency questions. Those questions provided requirements on the semantic model. The relevance of the semantic model was validated by implementing the competency questions into SPARQL queries enabling to query the constructed RDF knowledge graphs.
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hal-04495022 , version 1 (08-03-2024)

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  • HAL Id : hal-04495022 , version 1

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Nadia Yacoubi Ayadi, Stephan Bernard, Robert Bossy, Marine Courtin, Bill Gates Happi Happi, et al.. A Unified Approach to Publish Semantic Annotations of Agricultural Documents as Knowledge Graphs. I3S, Université Côte d'Azur; Paris Saclay University; INRAE; IRD. 2024, pp.43. ⟨hal-04495022⟩
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