Collaborative Research: Information Integration for Locating and Querying Geospatial Data

这项工作的目标是通过提供对不同数据集的语义整合，大大扩展地理空间社区创建的门户网站的能力。例如，威斯康星州土地信息系统和新的联邦地理空间一站式门户网站在紧急情况下传播数据并支持程序和模拟。然而，地理空间数据是复杂和高度异质的，是由各级政府和私营部门独立开发的。该项目包括元数据集成方法，以使数据源在网络上的位置更加精确，并为地理空间门户提供查询能力和信息集成类型的语义解析，这有助于信息发现、解决问题、该研究将开发方法和工具，以支持信息的整合，使最终用户能够有一个统一的视图异构数据源。将开发一种基于本体的体系结构，利用这种体系结构，每一个单独的异构数据源都可以以相对较少的努力被添加到信息网络中。本体映射将建立异构源中的术语与标准模型和本体中的术语之间的对应关系。该方法还扩展了本体映射纳入语义空间的考虑，如准确性，spatial integration.Information集成是一个研究领域，延伸到数据库，人工智能，数字图书馆和语义Web。该项目将通过提供一个强有力和可扩展的框架，包括使用基于本体的方法整合不同数据源的技术和算法，大大扩展一般信息整合和特别是地理空间信息整合的最新技术水平，该项目的地理空间数据语义整合目标符合在网上创建网络基础设施的广泛愿景。在地理空间网络基础设施中，数据将被自动定位并与其他相关数据源进行语义匹配。不需要手动干预或手动干预将最小化。有了这样一个结构，应急管理人员、政府官员和公众将不受预先制定的查询的限制。相反，可以进行专门的探索性查询和分析。从教育的角度来看，该项目将大大有利于培养研究生和本科生，特别是妇女和少数民族学生，并将包括设计新的研究生和本科生课程。

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.