TaToo: tagging environmental resources on the web by semantic annotations
The web is rapidly evolving and its traditional role of repository of static information is changing into a hub for collaboration among people. Web resources tend to become more and more complex, and to offer services that include access to remote databases, and computational power. All of this becomes very interesting not only for the common user, but especially for scientists and researchers which actually see their computers disappear into the web cloud, getting back an unprecedented access to services and computational resources. Yet, to exploit these new facilities new tools are needed. The TaToo project aims at exploiting a common practice among web user: search, discovery and tagging of interesting resources. The practice of tagging allows user groups to label and classify resources enabling aggregators to display the most relevant ones according to the context. TaToo aims to take the core idea of tagging and adding the ability to add valuable information in the form of semantic annotations, thus facilitating future usage and discovery, and kicking off a beneficial cycle of information enrichment. Thus, the production of semantic meta information will improve the discovery process, but also interpretation in a larger sense (verification that its the information I was looking for, assessment of usefulness for a given situation, understanding of how to use the information correctly etc.).
A. E. Rizzoli, G. Schimak, M. Donatelli, J. Hrebicek, G. Avellino, J. L. Mon, I. Athanasiadis, TaToo: tagging environmental resources on the web by semantic annotations, 5th Intl Congress on Environmental Modelling and Software (iEMSs 2010), 2010, International Environmental Modelling and Software Society (iEMSs).
You might also enjoy (View all publications)
- Mixing process-based and data-driven approaches in yield prediction
- Combining telecom data with heterogeneous data sources for traffic and emission assessments - an agent-based approach
- A weakly supervised framework for high-resolution crop yield forecasts