Collaborative Research: Information Integration for Locating and Querying Geospatial Data

这项工作的目标是通过在不同数据集上提供语义集成，显著扩展地理空间社区创建的门户的功能。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构的，由各级政府和私营部门独立开发。该项目包括元数据集成方法，以实现在网络上更精确地定位数据源，并为信息集成类型提供具有查询功能和语义解析的地理空间门户，从而有助于信息发现、问题解决或应急响应。该研究将开发方法和工具来支持信息集成，从而使最终用户对异构数据源具有同质视图。将开发一种基于本体的体系结构，通过这种体系结构，可以相对轻松地将每个单独的异构数据源添加到信息网络中。本体映射将在异构源中的术语与标准模型和本体的术语之间建立对应关系。该方法还扩展了本体映射，以合并关于空间考虑的语义，例如准确性，以实现空间集成。信息集成是一个横跨数据库、人工智能、数字图书馆和语义网的研究领域。该项目将通过提供包含使用基于本体的方法集成异构数据源的技术和算法的健壮且可扩展的框架，显著扩展一般信息集成，特别是地理空间信息集成的最新技术。这个项目的目标是对地理空间数据进行语义集成，这符合在Web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，数据将自动定位并与其他相关数据源进行语义匹配。人工干预将不需要或将是最小的。有了这样的结构，应急管理人员、政府官员和普通公众将不会受到预先制定的查询的限制。取而代之的是，特别的、探索性的查询和分析将成为可能。从教育角度来看，该项目将大大有利于培训研究生和本科生，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.