Résumé
Understanding genotype–phenotype relationships is one of the most important areas of research in agronomy. The new challenges aim at understanding these relationships on the level of the different molecular entities responsible for the expression of complex phenotypic traits. Recent advances in high-throughput technologies have resulted in tremendous increase in the amount of data in the agronomic domain. Unfortunately, they can only partially capture these dynamics. It is important to effectively integrate additional information and extract knowledge to understand the biological system as a whole. To this end, the Semantic Web offers a stack of powerful technologies for the integration of information from diverse sources, making knowledge explicit by the help of ontologies and explicit semantic relations between entities. In particular, knowledge graphs have gained popularity as means to structure and semantically represent the data and knowledge in a particular field, opening up new and enhanced ways of information retrieval and knowledge discovery. We have developed AgroLD, a knowledge graph that exploits the Semantic Web technology and some of the relevant standard domain ontologies, to integrate knowledge on plant crop species and in this way facilitate the formulation of new scientific hypotheses. This chapter provides an overview of the AgroLD project focusing on the data integration and semantic annotation processes which initially focused on genomics, proteomics, and phenomics. Likewise, we present the different data exploration strategies developed to make the platform available to a large audience. Our objective is to offer a domain specific knowledge platform to solve complex biological and agronomical questions related to the implication of genes in, for instance, plant disease resistance or high yield traits. Finally, we will present several current challenges in knowledge extraction from heterogeneous biological data sources.