%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Larmande, Pierre
%A Todorov, K.
%T AgroLD : a knowledge graph for the plant sciences
%B The semantic web - ISWC 2021
%C Cham
%D 2021
%E Hotho, A.
%E Blomqvist, E.
%E Dietze, S.
%E Fokoue, A.
%E Ding, Y.
%E Barnaghi, P.
%E Haller, A.
%E Dragoni, M.
%E Alani, H.
%L fdi:010083662
%G ENG
%I Springer
%@ 978-3-030-88360-7
%M ISI:000706991800029 
%P 496-510
%R 10.1007/978-3-030-88361-4_29
%U https://www.documentation.ird.fr/hor/fdi:010083662
%> https://www.documentation.ird.fr/intranet/publi/2022-02/010083662.pdf
%V 12922
%W Horizon (IRD)
%X Recent advances in sequencing technologies and high-throughput phenotyping have revolutionized the analysis in the field of the plant sciences. However, there is an urgent need to effectively integrate and assimilate complementary information to understand the biological system in its entirety. We have developed AgroLD, a knowledge graph that exploits Semantic Web technologies to integrate information on plant species and in this way facilitate the formulation and validation of new scientific hypotheses. AgroLD contains around 900M triples created by annotating and integrating more than 100 datasets coming from 15 data sources. Our objective is to offer a domain specific knowledge platform to answer complex biological and plant sciences questions related to the implication of genes in, for instance, plant disease resistance or adaptative responses to climate change. In this paper, we present results of the project, which focused on genomics, proteomics and phenomics. We present the AgroLD pipeline for lifting the data, the open source tools developed for these purposes, as well as the web application allowing to explore the data.
%S Lecture Notes in Computer Science
%B ISWC 2021 : International Semantic Web Conference
%8 2021/10/24-28
%$ 122APPLIC ; 076AMEPLA