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
Semantic relations are an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. Automatic semantic relation extraction system is a crucial tool that can reduce the bottleneck of knowledge acquisition in the ontologies construction. In this paper, we present a statistical approach for learning the semantic relations between concepts of an ontology in the agricultural domain. The semantic relations are acquired by using verbs to indicate the relations between ontology concepts. The co-occurrences of domain-verbs with their components, which are annotated the concepts, are analyzed by using several statistical methodologies. Moreover, we expand the sets of verb expressing the same semantic relation by using the extracted patterns of concept pairs of the seed verb's component. Our experiment has been done on a collection of Thai shallow parsed texts in the domain of agriculture. The precision and recall of the presented system is 65% and 82%, respectively.
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