Integrating Geoscience Ontologies with Dolce

Cross-disciplinary e-Science can be enabled by using foundational ontologies such as Dolce to integrate knowledge representations from different geoscience domains. G eoscientists are increasingly concerned with big problems related to climate change, natural hazards, and environmental health. In solving these problems, they're regularly encountering data and knowledge that are complex, diverse, distributed, and massive, causing them to turn to e-Science for operational aids. Useful e-Science resources such as high-performance computing grids, sensor networks, and large-scale data integration and modeling capabilities enable greater volumes of data to be collected in situ and then processed by distributed systems aimed at stimulating new scientific knowledge. Although the new knowledge sometimes includes new concepts and theories, it more frequently involves new predictive models of reality that exhibit dramatically increased geospatial resolution and thematic complexity. E-Science is thus becoming more knowledge-driven via its reliance on knowledge representations to achieve scientific goals. For many of the big problems, this requires geoscientists to represent and integrate knowledge from different science domains , which contrasts with recent trends in which integration is concentrated within single scientific domains. For example, groundwater pollution estimation requires representation and integration of data and knowledge from at least geology, hydroge-ology, soils, and topography. These in turn often require representation of data and knowledge at finer scales such as those described by chemistry and physics. However, existing ontologies are generally designed for data integration within single geosci-ence domains at specific scales—for example, for earthquake simulation, severe-storm prediction, or virtual solar observation—and not for cross-disciplinary use. Science knowledge in general, including geosci-ence, is represented implicitly and explicitly. It is represented implicitly in scientist's heads and in the undeclared concepts behind the structural components of formal schemas, such as those used to structure data repositories, data transfer formats, Web service signatures, and automated workflow specifications. Explicit representations include informal expressions of geoscience classifications,