Editor: Modeling with the Semantic Web in the Geosciences IEEE INTELLIGENT SYSTEMS Published by the IEEE Computer Society
T h e S e m a n t i c W e b numerous interacting components, each of which can be further dissected into subcomponents that specialists in a wide range of disciplines can study. This description makes evident the problems of both model interoperability and model simulator interoperability. Given the...
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| Format: | Text |
| Language: | English |
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1086.5191 http://sfpsy.org/spe-grape/epique-2005/Modeling%20with%20the%20semantic%20web.pdf |
| Summary: | T h e S e m a n t i c W e b numerous interacting components, each of which can be further dissected into subcomponents that specialists in a wide range of disciplines can study. This description makes evident the problems of both model interoperability and model simulator interoperability. Given the task's complexity and the number of research groups and individuals involved, there's a wide diversity of modeling approaches, such as models based on differential equations or stochastic methods. These approaches make difficult not only the interoperation of model specifications but also the intercomparison of models' structure and results, as is evident in the work of the Global Analysis, Integration, and Modeling Task Force (GAIM). 1 Similarly, in terms of simulator interoperability, models are developed in a broad range of programming languages and software, making it difficult to couple a Fortran model of thermohaline circulation with an ice sheet model in C++. Compounding these concerns are spatial-data issues. Spatial data form a primary input for models, and, as with all other types of data, its volume continues to grow at an explosive rate. 2 Yet, worldwide the national clearinghouses for spatial data are experiencing a decline in use, management, and content owing to the community's dissatisfaction with the functional capability of the portals providing such data. These problems largely derive from a common lack of explicit semantics in representing models, 5 spatial data, and scientific knowledge in general. Process ontologies To model earth system processes, we need ontologies in order to develop conceptually sound models, effectively communicate these models, enhance interoperability between models developed in different domains, and provide the opportunity for model components' reuse and sharing. To accomplish these goals, we must express these processes not only in terms of their types and properties but also in terms of their behavior, spatial and temporal characteristics, relationships to other ... |
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