基于本体的空间信息智能服务传输机理研究进展

本文首先阐述了本体在地理信息服务中的研究成果及其发展趋势,指出了目前没有将本体充分应用于空间信息智能化服务的现状。论文在研究了空间信息服务传输机理的基础上,阐述了空间信息服务传输的基本原理和所涉及的信息转换过程,并提出了针对各个过程建立相应本体来进行信息转换的思想,进而为空间信息的智能化服务探索一条解决方案。     

基于本体的地球空间信息上下文建模

总结分析了典型的地球空间信息上下文,基于本体建立了面向地球空间信息服务形式化的上下文信息模型,该模型采用层次化的设计方法,并使用本体描述语言OWL描述上下文,提高了上下文表达能力。最后,设计了一个面向地球空间信息的上下文感知计算模型。     

基于本体协同的空间信息互操作方法

提出了基于标准空间信息本体和混合结构语义本体协同的地理信息系统互操作，通过本体的协同实现基于标准空间本体的异构空间信息互通和基于异构语义本体分级匹配的空间信息重分类及语义互操作。     

为地理知识库构建位置本体

随着网络中各种地理相关的应用越来越多,要想解决地理空间信息语义层次上的应用问题,就需要用到地理知识库。其中本体是地理知识库的核心。地理本体是研究地理信息科学领域内不同层次和不同应用方向上的地理空间信息概念的详细内涵和层次关系。本文为地理知识库构建了极具实用性的的位置本体,并详细阐述了基于ETL的数据知识库从资源采集,到数据集成,最后到本体构建的过程。     

一种新的地理本体构建方法分析

对于地理本体的研究是在考虑地理信息自身特殊性的基础上,把本体在信息科学中的含义移植到地理信息科学中,实现对空间信息语义的丰富。目前没有一个标准的地理本体构造方法。本文针对当前地理本体构建方法的不足,提出了一种新的地理本体构建方法。该方法强调需求分析的重要性,采用螺旋式上升的构建过程,构建了海运领域地理本体,实现了海运地理信息的语义共享。     

一种新的地理本体构建方法分析

对于地理本体的研究是在考虑地理信息自身特殊性的基础上,把本体在信息科学中的含义移植到地理信息科学中,实现对空间信息语义的丰富。目前没有一个标准的地理本体构造方法。本文针对当前地理本体构建方法的不足,提出了一种新的地理本体构建方法。该方法强调需求分析的重要性,采用螺旋式上升的构建过程,构建了海运领域地理本体,实现了海运地理信息的语义共享。     

空间信息语义网格

在分析本体和网格技术的基础上,针对空间信息系统对信息语义的特殊要求,提出了空间信息语义网格的概念,并用本体构造了空间信息语义网格体系结构。     

基于P2P和本体的空间信息服务发布与发现

在开放分布的网络环境中发现并获取空间信息服务是GIS领域研究的热点问题,而目前空间信息服务注册中心各自独立的现状造成跨领域查询困难,且缺少语义支持的服务匹配也造成服务查准率低,这些都成为制约其发展的瓶颈.提出了基于P2P和本体的空间信息服务注册模型PeerRegistry,首先描述了模型结构、内部模块和服务发布、发现流程,利用本体推理机在全局本体和局部本体映射的基础上进行服务匹配,实现了服务的自动发布和一站式服务发现,并通过创建语义路由表提高对等发现过程中消息传递的效率.     

基于Jena和地理本体的空间查询与推理研究

Jena根据OWL本体定义将地理数据进行RDF资源标注,形成带有语义信息的数据,便于地理空间数据的语义查询和推理。深入讨论Jena在基于地理本体的空间信息查询和推理中的应用机制,将用户针对数据源的查询转换为对地理本体的查询;利用建立在逻辑基础上和空间关系基础上的地理本体,发现空间数据源中一些隐含的概念或关系。结合实例,实现基于本体的地理空间信息查询和在自定义规则下利用Jena推理机的空间推理。     

地理本体与空间信息多级网格

为解决空间数据分布异构的问题,根据地理本体与地理网格的特点,提出一种将地理本体映射到地理网格的新方法,并用该方法构造一种基于本体的空间信息网格系统。该系统通过描述空间信息的语义内涵的本体系统对空间信息数据进行索引和组织,并以地理网格为其存储和管理单元,可以有效地解决在广域网络环境下的空间信息资源整合的问题,促进空间信息共享与利用的研究。     

基于语义Web的地理服务发现框架

在传统Web服务发现架构中,仅凭接口参数的结构化相似度计算匹配方法远远不能满足复杂地理服务的发现需求。本文结合语义Web知识,设计了地理服务发现框架,框架支持地理服务语义化描述、带有语义信息的服务注册、基于语义推理的服务发现。其中,服务匹配算法是服务发现的关键,本文基于传统的四级匹配算法,提出采用分级匹配思想,并在I/O匹配中利用本体分类树,将本体相似度求解转换为分类树中节点距离求解的方法的地理服务发现匹配算法。实例证明,改进的算法不但能区分匹配等级,而且能区分同一匹配等级之间的相似度大小,能较好地满足地理服务的发现的需求。     

顾及语义知识的地理空间数据快速检索

大数据时代地理空间资源不断增多,但现有通用知识库较少考虑地理空间数据蕴含的语义知识,难以实现数据的快速检索.因此亟需引入本体技术,以蕴含的语义知识为基础,提高地理空间数据访问速度,精确获取用户所需信息.以本体为基础,提出了顾及地理空间数据语义知识的快速检索方法.首先,基于通名编码规则、地理空间数据和开源百度百科数据构建...     

地名本体的概念框架与构成研究

在提出地名本体的基本概念之后,根据基于地理空间语义网的日常地理信息查询需要,进行了地名本体的概念设计,提出了通过复用地名词典和地理主题词表构建地名本体的概念框架和设计方法;提出地名本体由地理实体本体、实体类型本体和空间关系本体3种地理本体构成,并详细介绍了其设计结构。     

Distributed geospatial information processing: sharing distributed geospatial resources to support Digital Earth

Abstract

This paper introduces a new concept, distributed geospatial information processing (DGIP), which refers to the process of geospatial information residing on computers geographically dispersed and connected through computer networks, and the contribution of DGIP to Digital Earth (DE). The DGIP plays a critical role in integrating the widely distributed geospatial resources to support the DE envisioned to utilise a wide variety of information. This paper addresses this role from three different aspects: 1) sharing Earth data, information, and services through geospatial interoperability supported by standardisation of contents and interfaces; 2) sharing computing and software resources through a GeoCyberinfrastructure supported by DGIP middleware; and 3) sharing knowledge within and across domains through ontology and semantic searches. Observing the long-term process for the research and development of an operational DE, we discuss and expect some practical contributions of the DGIP to the DE.     

Geospatial Ontology Development and Semantic Analytics

Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves specification of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations terms, such as near or surrounded by, and enable computation of spatiotemporal thematic proximity measures we define. SWETO (Semantic Web Technology Evaluation Ontology) and geospatial extension SWETO‐GS are examples of these ontologies. The Geospatial Semantics Analytics (GSA) framework incorporates: (1) the ability to automatically and semi‐automatically tract metadata from syntactically (including unstructured, semi‐structured and structured data) and semantically heterogeneous and multimodal data from diverse sources; and (2) analytical processing that exploits these ontologies and associated knowledge bases, with integral support for what we term spatiotemporal thematic proximity (STTP) reasoning and interactive visualization capabilities. This paper discusses the results of our geospatial ontology development efforts as well as some new semantic analytics methods on this ontology such as STTP.     

Geospatial Information Integration for Authoritative and Crowd Sourced Road Vector Data

This article describes results from a research project undertaken to explore the technical issues associated with integrating unstructured crowd sourced data with authoritative national mapping data. The ultimate objective is to develop methodologies to ensure the feature enrichment of authoritative data, using crowd sourced data. Users increasingly find that they wish to use data from both kinds of geographic data sources. Different techniques and methodologies can be developed to solve this problem. In our previous research, a position map matching algorithm was developed for integrating authoritative and crowd sourced road vector data, and showed promising results ( Anand et al. 2010 ). However, especially when integrating different forms of data at the feature level, these techniques are often time consuming and are more computationally intensive than other techniques available. To tackle these problems, this project aims at developing a methodology for automated conflict resolution, linking and merging of geographical information from disparate authoritative and crowd‐sourced data sources. This article describes research undertaken by the authors on the design, implementation, and evaluation of algorithms and procedures for producing a coherent ontology from disparate geospatial data sources. To integrate road vector data from disparate sources, the method presented in this article first converts input data sets to ontologies, and then merges these ontologies into a new ontology. This new ontology is then checked and modified to ensure that it is consistent. The developed methodology can deal with topological and geometry inconsistency and provide more flexibility for geospatial information merging.     

A Task-Based Ontology Approach to Automate Geospatial Data Retrieval

This paper presents a task‐based and Semantic Web approach to find geospatial data. The purpose of the project is to improve data discovery and facilitate automatic retrieval of data sources. The work presented here helps create the beginnings of a Geospatial Semantic Web. The intent is to create a system that provides appropriate results to application users who search for data when facing tasks such as emergency response or planning activities. In our task‐based system, we formalize the relationships between types of tasks, including emergency response, and types of data sources needed for those tasks. Domain knowledge, including criteria describing data sources, is recorded in an ontology language. With the ontology, reasoning can be done to infer various types of information including which data sources meet specific criteria for use in particular tasks. The vision presented here is that in an emergency, for example, a user accesses a Web‐based application and selects the type of emergency and the geographic area. The application then returns the types and locations (URLs) of the specific geospatial data needed. We explore the abilities and limitations of the OWL Web Ontology Language along with other Semantic Web technologies for this purpose.     

一种从空间数据库中提取地理本体的方法

传统的GIS应用以空间数据库为中心进行组织,而异构的空间数据库之间因为缺乏被计算机所理解的语义知识,很难解决日益增长的异构的GIS应用之间的互操作的需求。本体（ontology）技术被看成是解决不同应用系统之间的异构性以及互操作难题的一个重要途径。传统的地理本体需要通过领域专家人工建立,比较耗费时间。本文提出一种从已经存在的空间数据库中提取出地理本体的方法,来解决异构系统中本体获取困难的问题。