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Cross-disciplinary e-science can be enabled by using foundational ontologies such as Dolce to integrate knowledge representations from different geoscience domains. Geoscientists 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.