Collaborative Research: Information Integration for Locating and Querying Geospatial Data

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The objective of this work is to extend significantly the capabilities of portals created by the geospatial community, by providing semantic integration over diverse data sets. For example, the Wisconsin Land Information System and the new Federal Geospatial One-Stop portals disseminate data and support procedures and simulations in emergency situations. However, geospatial data are complex and highly heterogeneous, having been developed independently by various levels of government and the private sector. This project includes metadata integration methods to enable more precise location of data sources over the web and to provide geospatial portals with query capabilities and semantic resolution for the types of information integration that could help in information discovery, problem-solving, or emergency response.The research will develop methods and tools to support the integration of information in such a way that end users will have a homogeneous view over heterogeneous data sources. An ontology-based architecture will be developed with which each individual heterogeneous data source can be added to the network of information with relatively little effort. Ontology mappings will establish correspondences between terms in heterogeneous sources and those of standard models and ontologies. The approach also extends ontology mappings to incorporate semantics regarding spatial considerations, such as accuracy, for spatial integration.Information integration is an area of research that stretches over databases, artificial intelligence, digital libraries, and the semantic web. This project will extend significantly the state of the art of information integration in general and of geospatial information integration in particular, by providing a robust and scalable framework that encompasses techniques and algorithms for integrating heterogeneous data sources using an ontology-based approach.This project's goal of semantic integration for geospatial data fits into a broad vision for creating a cyberinfrastructure on the Web. In a geospatial cyberinfrastructure, data will be automatically located and semantically matched to other relevant data sources. Manual intervention will not be needed or will be minimal. With such a structure, emergency managers, government officials, and the general public will not be constrained to pre-formulated queries. Instead, ad hoc, exploratory queries and analyses will be possible. From an educational viewpoint, the project will significantly benefit the training of graduate and undergraduate students, in particular of women and minorities, and will include the design of new graduate and undergraduate courses.