CRII: III: RI: Empowering Multi-Conceptual Spatial Reasoning with a Repository of Qualitative and Quantitative Spatial Ontologies

点击翻译按钮获取中文摘要

People have manifold ways to express and process spatial information. As needed, they employ conceptualizations of space, so-called cognitive maps, that differ in granularity, scope, and precision. Based on the context, people use different implicit assumptions to interpret a spatial relation such as "A is contained in B." The assumptions made or implied by different conceptualizations often even contradict another (e.g., in "the lake contains a bay" the bay is a subregion of the lake while in "the lake contains an island" the island is surrounded by the lake). This is not an issue for people: we are able to quickly and reliably decide which conceptualization is most suitable in a specific situation, often choosing the simplest applicable conceptualization. For instance, people can quickly make navigation decisions ("Do I need to travel north or south on the Interstate?") or answer simple spatial queries ("Is the ocean east or west from here?"). Similar capabilities for flexibly utilizing multiple conceptualizations would make computational tools for recording and processing spatial information much more powerful and user-friendly. Towards this goal, the proposed research will investigate a formalism and basic procedures for automatically choosing a spatial representation best suited to solve a specific task, such as finding data with certain qualitative characteristics, answering a spatial query, or testing a spatial hypothesis. The research will contribute to a theoretical foundation for collecting, accessing, and manipulating spatial information in more natural ways without impeding its efficient processing in information systems. It will lower the barrier of entry for interacting with and analyzing spatial information and promotes technologies to cut time and costs typically spent on transforming diverse spatial data sets into a coherent model.In this project, the different spatial conceptualizations will be encoded as machine-interpretable spatial ontologies and placed in a structured ontology repository that leverages relationships from mathematical logic. The formal foundation for comparing the expressivity of ontologies using the mathematical notion of definability will be developed and used to organize the repository by differences in ontological assumptions and expressivity. This structure will be exploited by procedures for automatically selecting an ontology that best fits a specific spatial task. In addition, formal encodings of the knowledge necessary to convert spatial information from one ontology to another will be investigated, accompanied by procedures that utilize this information for automatically identifying and converting pieces of geometric background knowledge relevant to a specific spatial task. The research in this project will contribute to a better understanding of how logical relationships can be utilized to formally compare the expressiveness of two spatial ontologies, and how a spatial ontology repository can be supplemented by information that allows automated conversion of knowledge based on different ontologies. For further information see the project web page: http://www.spatial.maine.edu/~torsten/projects/QQSOR.shtml