Towards a Semantically Unified Environmental Information Space
In recent years we have witnessed a proliferation of environmental information on the Web thanks to advances in automated data acquisition and to the widespread use of computer based models and decision support systems processing environmental data. The number of environmental data providers has been also increasing. However, each provider manages its own data sets encoded into specific data formats and unaware of related and relevant data managed by other providers. Also, most of the environmental data providers store their data into huge, centralized repositories, which makes the access and discovery of desired data difficult. The Linked Data principles along with the Semantic Web technologies have been recognized as a promising solution to both environmental data integration and discovery. Unique identification of environmental data by HTTP dereferencable URIs, semantic annotation of environmental data by shared domain conceptualizations (ontologies), and interlinking of related environmental data by typed (semantic) links will enable the integration of disconnected environmental data sets into the semantically unified environmental information space. Semantic annotations and semantic links will then enable semantic discovery of environmental data over such unified information space. In this paper, we try to identify a number of requirements that environmental data providers should satisfy in order to make their data fully contribute to this vision. In particular, we are focused on requirements regarding environmental data identification, representation, annotation and linking.
S. Nesic;, A. E. Rizzoli, I. N. Athanasiadis, Towards a Semantically Unified Environmental Information Space, Environmental Software Systems, IFIP Advances in Information and Communication Technology, vol. 359, pg. 407-418, 2011, Springer, doi:10.1007/978-3-642-22285-6_44.
You might also enjoy (View all publications)
- Introducing digital twins to agriculture
- Location-specific vs location-agnostic machine learning metamodels for predicting pasture nitrogen response rate
- Machine learning for large-scale crop yield forecasting