Collaborative Research: Information Integration for Locating and Querying Geospatial Data

这项工作的目标是通过在不同的数据集上提供语义集成，显著扩展由地理空间社区创建的门户的能力。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和支持程序和模拟。然而，地理空间数据是复杂和高度异质的，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Web上更精确地定位数据源，并为地理空间门户提供查询功能和语义解析，以帮助发现信息、解决问题或应急响应信息集成类型。研究将开发支持信息集成的方法和工具，以使最终用户对不同类型的数据源有统一的看法。将开发一个基于本体的架构，利用该架构，每个独立的不同类型的数据源可以相对较少的工作就添加到信息网络中。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。该方法还扩展了本体映射，以纳入关于空间考虑的语义，如准确性，用于空间集成。信息集成是一个跨越数据库、人工智能、数字图书馆和语义网的研究领域。该项目将通过提供一个健壮的、可扩展的框架，包括使用基于本体的方法集成不同的数据源的技术和算法，极大地扩展信息集成的总体水平，特别是地理空间信息集成的水平。该项目的地理空间数据语义集成的目标符合在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，数据将被自动定位并与其他相关数据源进行语义匹配。将不需要或将最小限度地进行人工干预。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局限于预先制定的问题。取而代之的是，特别的探索性查询和分析将成为可能。从教育的角度来看，该项目将极大地有利于研究生和本科生的培训，特别是对妇女和少数群体的培训，并将包括设计新的研究生和本科课程。

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.