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
Model-Driven Knowledge-Based Development of Expected Answer Type Taxonomies for Restricted Domain Question Answering
A Question Answering (QA) system must provide concise answers from large collections of documents to questions stated by the user in natural language. Importantly, a question should be correctly classified by means of a predefined taxonomy in order to determine which is the Expected Answer Type (EAT), thus reducing the searching space over documents, while a right answer is obtained. Designing a proper EAT taxonomy is even more crucial in restricted domain QA, since domain experts use specific terminology, thus asking more precise questions and expecting more precise answers. This paper presents a novel model-driven approach in order to ameliorate the task of designing restricted-domain EAT taxonomies by using heterogeneous knowledge resources and collection of documents. To show the applicability of our approach, a set of experiments has been carried out by defining a new EAT taxonomy for being able to answer questions about the agricultural domain.
Inappropriate format for Document type, expected simple value but got array, please use list format