The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
In the agricultural sector, the improvement of productivity and quality with respect to such attributes as safety, security and taste has been required in recent years. We aim to contribute to such improvement through the application of Information and Communication Technology (ICT). In this paper, we first propose a model of agricultural knowledge by Linked Open Data (LOD) with a view to establishing an open standard for agricultural data, allowing flexible schemas based on ontology alignment. We also present a semi-automatic mechanism that we developed to extract agricultural knowledge from the Web, which involves a bootstrapping method and dependency parsing, and confirmed a certain degree of accuracy. Moreover, we present a voice-controlled question-answering system that we developed for the LOD using triplification of query sentences and graph pattern matching of the triples. Finally, we confirm through a use case that users can obtain the necessary knowledge for several problems encountered in the agricultural workplace.
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