The Crop Ontology (CO) of the Generation Challenge Program (GCP) (http://cropontology.org/) is developed for the Integrated Breeding Platform (IBP) (http://www.integratedbreeding.net/) by several centers of The Consultative Group on International Agricultural Research (CGIAR): bioversity, CIMMYT, CIP, ICRISAT, IITA, and IRRI. Integrated breeding necessitates that breeders access genotypic and phenotypic data related to a given trait. The CO provides validated trait names used by the crop communities of practice (CoP) for harmonizing the annotation of phenotypic and genotypic data and thus supporting data accessibility and discovery through web queries. The trait information is completed by the description of the measurement methods and scales, and images. The trait dictionaries used to produce the Integrated Breeding (IB) fieldbooks are synchronized with the CO terms for an automatic annotation of the phenotypic data measured in the field. The IB fieldbook provides breeders with direct access to the CO to get additional descriptive information on the traits. Ontologies and trait dictionaries are online for cassava, chickpea, common bean, groundnut, maize, Musa, potato, rice, sorghum, and wheat. Online curation and annotation tools facilitate (http://cropontology.org) direct maintenance of the trait information and production of trait dictionaries by the crop communities. An important feature is the cross referencing of CO terms with the Crop database trait ID and with their synonyms in Plant Ontology (PO) and Trait Ontology (TO). Web links between cross referenced terms in CO provide online access to data annotated with similar ontological terms, particularly the genetic data in Gramene (University of Cornell) or the evaluation and climatic data in the Global Repository of evaluation trials of the Climate Change, Agriculture and Food Security programme (CCAFS). Cross-referencing and annotation will be further applied in the IBP.

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Ontology engineering and knowledge modeling for the plant sciences is expected to contribute to the understanding of the basis of plant traits that determine phenotypic expression in a given environment. Several crop-or clade-specific plant trait ontologies have been developed to describe plant traits important for agriculture in order to address major scientific challenges such as food security. We present three successful species and/or clade-specific ontologies which address the needs of crop scientists to quickly access a wide range of trait related data, but their scope limits their interoperability with one another. In this paper, we present our vision of a species-neutral and overarching Reference Plant Trait Ontology which would be the basis for linking the disparate knowledge domains and that will support data integration and data mining across species. use terms consistently in scientific publications or standardized handbooks on quality/trait evaluations, and to search for and integrate data linked to these terms in anatomical, genetic, genomic, and other types of biological databases.

Language resources are a cornerstone of linguistic research and for the development of natural language processing tools, but the discovery of relevant resources remains a challenging task. This is due to the fact that relevant metadata records are spread among different repositories and it is currently impossible to query all these repositories in an integrated fashion, as they use different data models and vocabularies. In this paper we present a first attempt to collect and harmonize the metadata of different repositories, thus making them queriable and browsable in an integrated way. We make use of RDF and linked data technologies for this and provide a first level of harmonization of the vocabularies used in the different resources by mapping them to standard RDF vocabularies including Dublin Core and DCAT. Further, we present an approach that relies on NLP and in particular word sense disambiguation techniques to harmonize resources by mapping values of attributes-such as the type, license or intended use of a resource-into normalized values. Finally, as there are duplicate entries within the same repository as well as across different repositories, we also report results of detection of these duplicates.