e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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.

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Data-Driven Schema Matching in Agricultural Learning Object Repositories


As the wealth of structured repositories of educational content for agricultural object is increasing, the problem of heterogeneity between them on a semantic level is becoming more prominent. Ontology matching is a technique that helps to identify the correspondences on the description schemas of different sources and provide the basis for interesting applications that exploit the information in a linked fashion. The present paper presents a data-driven approach for discovering matches between different classification schemas. The approach is based on content analysis and linguistic processing in order to extract information in the form of relation tuples, use the extracted information to associate the content of different repositories and match their underlying classification schemas based on the degree of content similarity. The preliminary results verified the validity of the approach, as both experiments produced a semantically valid matching in 68% of the examined classes. The results also exposed the need for refinements on the linguistic processing of the available textual information and on the definition of relation similarity, as well as, the need to exploit structural information in order to move from discovering semantically valid matches to effectively handling class specializations and generalizations.

  • GR
  • ES
  • Univ_Alcala_UAH (ES)
  • AgroKnow (GR)
  • Univ_Piraeus (GR)
Data keywords
  • ontology
  • semantic
Agriculture keywords
  • agriculture
Data topic
  • knowledge transfer
  • modeling
  • semantics
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
  • Univ_Alcala_UAH (ES)
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e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.