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
Named Entity recognition is a challenging problem in the area of text mining. Most of the work in this field has been done in open domain. However the notion of named entity changes when we move from open domain to domain specific NER. For domain specific NER only the biomedical domain has been taken in consideration. While in Agriculture domain no significant work has been done. Thus NER for Agriculture Domain becomes an interesting research problem. In this work we have taken a step to develop a NER for agriculture domain namely AGNER. We have used linguistic and domain specific knowledge base for developing the system. We have used Agrovoc hierarchy for labeling the terms with the appropriate tags. Two layers of tags namely: <Fine grain> and <Coarse Grain> has been used to label each agriculture terms in the dataset.
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