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
This work exploits the logical structure of information rich texts to automatically annotate text segments contained within them using a domain ontology. The underlying assumption behind this work is that segments in such documents embody self contained informative units. Another assumption is that segment headings coupled with a document's hierarchical structure offer informal representations of segment content; and that matching segment headings to concepts in an ontology/thesaurus can result in the creation of formal labels/meta-data for these segments. When an encountered heading can not be matched with any concepts in the ontology, the hierarchical structure of the document is used to infer where a new concept represented by this heading should be added in the ontology. So, in this work the bootstrap ontology is also enriched by new concepts encountered within input documents. This paper also presents issues/problems related to matching textual entities to concepts in an incomplete ontology. The approach presented in this paper was applied to a set of agricultural extension documents. The results of carrying out this experiment demonstrates that the proposed approach is capable of automatically annotating segments with concepts that describe a segment's content with a high degree of accuracy.
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