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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Using Ontology to Harmonize Knowledge Concepts in Data and Models


Policy makers are confronted with complex, socially relevant problems. To increase the quality of their policy and realize a broader support for and understanding of actions, the policy making process has become a participatory process where policy measures need to be assessed in an integrated context (Rotmans and van Asselt,1996). In these integrated assessment studies, people work from different perspectives and domains, e. g. from an agricultural modelling perspective, an environmental perspective or an economic policy/problem perspective. Semantic interoperability is the key factor to integrate the knowledge of these different domains and perspectives in a computerized framework. Semantic interoperability is the ability of systems/components to share and understand information at the level of formally defined and mutually accepted domain concepts (Solvberg, 1998). The specification of these concepts is done with ontology. One of the most cited definitions of ontology is from Gruber (1993): "an explicit and formal specification of a conceptualization". A "conceptualization" can be considered as an abstract model for a phenomenon identifying the relevant concepts. All concepts are explicitly described in a formal machine readable language. The challenge in integrated modeling is the conceptual integration. To achieve this, we need explicit semantics and a shared conceptualization. For this we need to tackle the different perceptions and interpretations of people involved. Different modeling approaches, different formalism and last but certainly not least, the different integration requirements and ambitions need to be taken into account. In the SEAMLESS project (Van Ittersum et al., 2007), the process used for creating a common ontology for models, indicators and raw data is based on a participatory and collaborative approach. A dedicated taskforce was created with participants from different parts of the project, and coordinated by the work package charged with integration. This task force envisages to develop a common knowledge base that represents a shared conceptualization between the different databases, models and indicators, with adequate meta-data. A core component, called the SEAMLESS Knowledge Manager (KM) (Villa et al., 2007), provides functionality as an extensible semantic modeling toolkit. It loads meaning through OWL (Ontology Web Language) ontology and transparently connects formal concepts to software objects and literals. Scalable use of machine reasoning effectively integrates an object-oriented framework, an object oriented database system and an ontology-based knowledge management environment into a SEAMLESS whole. The development of the SEAMLESS common ontology was and still is a big challenge. By putting ontology in a central position in the project and the systems architecture, this shared conceptualization is the basis for generating (Java) source code for the object classes representing all the concepts and representing the objects in relational database tables. The use of ontology has proved to be very useful if not essential both for the technical integration of knowledge in the SEAMLESS Integrated Framework and in understanding the meaning of communicated words of the diversity of people within the project.

  • NL
  • Wageningen_Univ_and_Res_Ctr_WUR (NL)
Data keywords
  • knowledge
  • ontology
  • knowledge based
  • semantic
  • reasoning
  • OWL
  • agricultural model
Agriculture keywords
  • agriculture
Data topic
  • modeling
  • semantics
Document type

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

Institutions 10 co-publis
  • Wageningen_Univ_and_Res_Ctr_WUR (NL)
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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.