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
Learning ontology from text is a challenge in knowledge engineering research and practice. Learning relations between concepts is even more difficult work. However, when considering only a particular domain in which the concept hierarchy and relations can be modeled manually within an acceptable period of time, the learning process may be simplified. We focus on learning composite concepts and building up a knowledge base from existing documents. Our approach tries to make the machine understand the documents sentence by sentence and finally fit the knowledge conveyed by the document in our pre-defined ontology. Basic semantic units are defined for reasoning with higher-level concepts, including classes and instances. An agricultural case study on learning instances from plant disease descriptions is presented with a web-based ontology learning tool.
Inappropriate format for Document type, expected simple value but got array, please use list format