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
Most of the methods for ontology merging need participation of domain experts and provide only partial automation with low efficiency. It is time-consuming and laborious especially when large amount of data are to be merged. In this paper, a method based on semantic is designed for ontology merging according to the case of ontologies to be merged. In this method, first the ontologies to be merged are conversed into a unified format. Then a secondary file is generated. After that semantic analysis is applied to secondary file and a dynamic SQL statements is generated automatically. At last the SQL statements are executed to finish ontology merging. It has been proved by the experiment that using this method would get good precision and high efficiency. Using this method, two ontologies Chinese Agricultural Thesaurus and AGROVOC of the Food and Agriculture Organization are merged successfully.
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