Semantics for interoperability of distributed data and models: Foundations for better-connected information
Correct and reliable linkage of independently produced information is a requirement to enable sophisticated applications and processing workflows. These can ultimately help address the challenges posed by complex systems (such as socio-ecological systems), whose many components can only be described through independently developed data and model products. We discuss the first outcomes of an investigation in the conceptual and methodological aspects of semantic annotation of data and models, aimed to enable a high standard of interoperability of information. The results, operationalized in the context of a long-term, active, large-scale project on ecosystem services assessment, include:
- A definition of interoperability based on semantics and scale;
- A conceptual foundation for the phenomenology underlying scientific observations, aimed to guide the practice of semantic annotation in domain communities;
- A dedicated language and software infrastructure that operationalizes the findings and allows practitioners to reap the benefits of data and model interoperability.
The work presented is the first detailed description of almost a decade of work with communities active in socio-ecological system modeling. After defining the boundaries of possible interoperability based on the understanding of scale, we discuss examples of the practical use of the findings to obtain consistent, interoperable and machine-ready semantic specifications that can integrate semantics across diverse domains and disciplines.
F. Villa, S. Balbi, I. N. Athanasiadis, C. Caracciolo, Semantics for interoperability of distributed data and models: Foundations for better-connected information, F1000Research, 6 2017, doi:10.12688/f1000research.11638.1.
You might also enjoy (View all publications)
- The flows of nature to people, and of people to nature: applying movement concepts to ecosystem services
- Crop2ML: An open-source multi-language modeling framework for the exchange and reuse of crop model components
- CropGym: a Reinforcement Learning Environment for Crop Management