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
Rapid development and deployment of bi-directional expert systems using machine translation technology
The present work reports our attempt in developing an English-Arabic bi-directional machine translation tool in the agriculture domain. It aims to achieve automated translation of agricultural expert systems. In particular, we describe the translation of domain knowledge base, including, prompts, responses, explanation text, and advices. In the Central Laboratory for Agricultural Expert Systems (CLAES) where many successful agricultural expert systems have been developed, this tool is found to be essential in developing bi-directional (English-Arabic) expert systems because both English and Arabic versions are needed for development, deployment, and usage purpose. The tool also helps knowledge engineers in overcoming the language barrier by acquiring knowledge from either English or Arabic speaking domain experts. This paper discusses our experience with the developed machine translation tool and reports on results of its application on real agricultural expert systems. (C) 2011 Elsevier Ltd. All rights reserved.
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