A MODEL-BASED FRAMEWORK FOR REPRESENTING GEOINFORMATION

‘Geoscientific space-time information’ (geoinformation) is increasingly viewed as an important scientific resource, one to be regularly shared, exchanged and integrated by geoscientists. The recent initiation of several government and academic geoinformation networks is evidence of this trend. However, sharing geoinformation is impeded by representations (such as various data models and standards) not originally designed for scientific purposes, thereby limiting the ability to adequately capture the various models that provide context and meaning to geoinformation. This gap is addressed in this work by the development of a prototype geoinformation representation architecture that integrates various types of geoscientific knowledge and models, including conceptual models (that are taxonomic, ontologic and theoretic), physical models (that are descriptive, structural or process-oriented), and cognitive models (that are personal, historical, and subjective). Various types of geoscientific knowledge imbued in such models, as well as the many challenges faced in representing these models (geo)informatically, will be discussed. The novel geoinformation architecture that addresses these challenges will be presented in four dimensions: (1) as a conceptual framework developed from work in the philosophy of (geo)science, cognitive science, and information science; (2) as proposed enrichments to object-oriented scientific computing; (3) as a set of emerging implementations being developed by numerous government data providers concerned with the development of national geologic map databases, standards, and seamless geoinformation networks in Canada and the US; and (4) in relation to some international geospatial information standards. Although examples will be drawn primarily from the geologic mapping domain, the prototype architecture is seen as promising and broadly applicable, having the potential to benefit the broader geoscience community by enhancing fundamental geoinformation representation, and eventually its interoperability and use.