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.     

Structural Alignment Methods with Applications to Geospatial Ontologies

We consider the problem of enabling interoperability and information sharing among geospatial applications that use ontologies to describe their concepts and the relationships among them. We present two fully automatic alignment methods that use the graph structures of a pair of ontologies to establish their alignment, that is, the semantic correspondences between their concepts. We have tested our methods on geospatial ontologies pertaining to wetlands and four other pairs that belong to a repository that has been used in the Ontology Alignment Evaluation Initiative (OAEI). Using these ontologies, we have compared the effectiveness (precision and recall) of our methods against the Similarity Flooding Algorithm that was proposed by others and show that for each of the tested ontologies one of our methods is at least as effective as their method. We have tuned the performance of our methods by introducing a greedy approach that reduces the number of concepts that get compared. This approach reduces runtime by approximately 30% with a minor compromise to the effectiveness of the results. To further validate our approach, we participated in the OAEI competition to align a pair of ontologies, each with a few thousand concepts.     

Ontology‐supported Querying of Geographical Databases

Querying geographical information systems has been recognized as a difficult task for non‐expert users. Furthermore, user queries are often characterized by semantic aspects not directly managed by traditional spatial databases or GIS. Examples of such semantic geospatial queries are the use of implicit spatial relations between objects, or the reference of domain concepts not explicitly represented in data. To handle such queries, we envisage a system that translates natural language queries into spatial SQL statements on a database, thus improving standard GIS with new semantic capabilities. Within this general objective, the contribution of this article is to introduce a methodology to handle semantic geospatial queries issued over a spatial database. This approach captures semantics from an ontology built upon the spatial database and enriched by domain concepts and properties specifically defined to represent the localization of objects. Some examples of the use of the methodology in the urban domain are presented.     

Using Ontologies for Integrated Geographic Information Systems

Today, there is a huge amount of data gathered about the Earth, not only from new spatial information systems, but also from new and more sophisticated data collection technologies. This scenario leads to a number of interesting research challenges, such as how to integrate geographic information of different kinds. The basic motivation of this paper is to introduce a GIS architecture that can enable geographic information integration in a seamless and flexible way based on its semantic value and regardless of its representation. The proposed solution is an ontology-driven geographic information system that acts as a system integrator. In this system, an ontology is a component, such as the database, cooperating to fulfill the system's objectives. By browsing through ontologies the users can be provided with information about the embedded knowledge of the system. Special emphasis is given to the case of remote sensing systems and geographic information systems. The levels of ontologies can be used to guide processes for the extraction of more general or more detailed information. The use of multiple ontologies allows the extraction of information in different stages of classification. The semantic integration of aerial images and GIS is a crucial step towards better geospatial modeling.     

Ontological Engineering for Interpreting Geospatial Queries

Despite advancements in geographic information system (GIS) technology, the efficient and effective utilization of GIS to solve geospatial problems is a daunting process requiring specialized knowledge and skills. Two of the most important and burdensome tasks in this process are interpretation of geospatial queries and mapping the interpreted results into geospatial data models and geoprocessing operations. With the current state of GIS, there exists a gap between the knowledge user's possess and the knowledge and skills they need to utilize GIS for solving problems. Currently, users resort to training and practice on GIS technology or involving GIS experts. Neither of these options is optimal and there is a need for a new approach that automates geoprocessing tasks using GIS technology. This paper presents an ontological engineering methodology that uses multiple ontologies and the mappings among them to automate certain tasks related to interpretation of geospatial queries and mapping the interpreted results into geospatial data models and geoprocessing operations. The presented methodology includes conceptualization of geospatial queries, knowledge representation for queries, techniques for relating elements in different ontologies, and an algorithm that uses ontologies to map queries to geoprocessing operations.     

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。     

A Rule-Based Strategy for the Semantic Annotation of Geodata

The ability to represent geospatial semantics is of great importance when building geospatial applications for the Web. This ability will enhance discovery, retrieval and translation of geographic information as well as the reuse of geographic information in different contexts. The problem of generating semantic annotations has been recognized as one of the most serious obstacles for realizing the Geospatial Semantic Web vision. We present a rule‐based strategy for the semantic annotation of geodata that combines Semantic Web and Geospatial Web Services technology. In our approach, rules are employed to partially automate the annotation process. Rules define conditions for identifying geospatial concepts. Based on these rules, spatial analysis procedures are implemented that allow for inferring whether or not a feature in a dataset represents an instance of a geospatial concept. This automated evaluation of features in the dataset generates valuable information for the creation and refinement of semantic annotations on the concept level. The approach is illustrated by a case study on annotating data sources containing representations of lowlands. The presented strategy lays the foundations for the specification of a semantic annotation tool for geospatial web services that supports data providers in annotating their sources according to multiple domain views.     

基于形式本体的基础地理信息语义分析——以陆地水系要素类为例

以国家基础地理信息中陆地水系要素为案例,讨论其规范定义中语义描述的模糊性。鉴于本体被认为是一种克服语义屏障的有效方法,本文基于形式本体的原理,提出一种对地理信息形式化语义分析的范例方法,即在概念化的基础上,利用属性组来表达概念语义,通过定义概念的本体属性来明确及规范化地表达概念的语义。通过具体分析,本文给出基础地理信息陆地水系要素的形式化语义。     

Approaches to Semantic Similarity Measurement for Geo-Spatial Data: A Survey

Semantic similarity is central for the functioning of semantically enabled processing of geospatial data. It is used to measure the degree of potential semantic interoperability between data or different geographic information systems (GIS). Similarity is essential for dealing with vague data queries, vague concepts or natural language and is the basis for semantic information retrieval and integration. The choice of similarity measurement influences strongly the conceptual design and the functionality of a GIS. The goal of this article is to provide a survey presentation on theories of semantic similarity measurement and review how these approaches – originally developed as psychological models to explain human similarity judgment – can be used in geographic information science. According to their knowledge representation and notion of similarity we classify existing similarity measures in geometric, feature, network, alignment and transformational models. The article reviews each of these models and outlines its notion of similarity and metric properties. Afterwards, we evaluate the semantic similarity models with respect to the requirements for semantic similarity measurement between geospatial data. The article concludes by comparing the similarity measures and giving general advice how to choose an appropriate semantic similarity measure. Advantages and disadvantages point to their suitability for different tasks.     

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...     

空间服务语义模式的地理信息服务发现

如何从大规模地理信息服务集合中快速且准确地发现目标服务是地理信息服务应用中的一个关键问题。当前基于关键字的服务发现方式缺乏语义支持,搜索效率低。本文在WSMO/WSML框架下,提出了一种基于空间服务语义模式的服务发现方法,将地理信息从语法模式转换为语义模式,明确表达空间数据中隐含的知识,有效克服数据源之间的语义异构。该方法能够显著提高地理信息服务发现的查全率和查准率。     

多源地理空间矢量数据关联模型设计

多源地理空间矢量数据之间存在着隐含的关联关系，这些关联关系往往隐式存在，难以直观展示，也难以与空间数据映射交互展示，更无法进行查询分析，获取所需信息。针对这种情况，本文以多源地理空间矢量数据及统计数据为研究对象，首先定义了多源地理空间矢量数据关联的概念及分类，然后以此为基础设计了多源地理空间矢量数据关联模型，并将其分为3个子模型：基于自适应四叉树编码的空间关联子模型、基于几何匹配的空间关联子模型及基于语义匹配的空间关联子模型。该模型定义了多源地理空间矢量数据之间的关联方式，为关联关系的构建奠定了理论基础。     

移动轨迹的尺度特征

移动轨迹是移动对象在地理空间环境中移动而留下的痕迹。移动轨迹数据是一类特殊的地理时空数据，它具有时间尺度、空间尺度和语义尺度特征。本文首先分析了轨迹数据的空间尺度特征与时间尺度特征，建立了轨迹空间尺度与时间尺度转换关系式；然后论述了轨迹的语义内涵和语义尺度特征，将轨迹语义分为移动对象语义、地理空间环境语义、采集设备语义、移动方式语义，并从地理空间环境语义的角度分析了轨迹的语义多尺度；最后探讨了轨迹语义尺度与时空尺度之间的一致性关系。     

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.     

A holistic approach to aligning geospatial data with multidimensional similarity measuring

Semantically aligning the heterogeneous geospatial datasets (GDs) produced by different organizations demands efficient similarity matching methods. However, the strategies employed to align the schema (concept and property) and instances are usually not reusable, and the effects of unbalanced information tend to be neglected in GD alignment. To solve this problem, a holistic approach is presented in this paper to integrally align the geospatial entities (concepts, properties and instances) simultaneously. Spatial, lexical, structural and extensional similarity metrics are designed and automatically aggregated by means of approval voting. The presented approach is validated with real geographical semantic webs, Geonames and OpenStreetMap. Compared with the well-known extensional-based aligning system, the presented approach not only considers more information involved in GD alignment, but also avoids the artificial parameter setting in metric aggregation. It reduces the dependency on specific information, and makes the alignment more robust under the unbalanced distribution of various information.     

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。     

顾及上下文的空间信息服务组合语义匹配方法

提出了一种顾及上下文的空间信息服务组合语义匹配方法。该方法根据空间信息服务组合的特点,充分考虑了空间信息组合服务对内部抽象原子服务匹配的约束,以及匹配过程中各抽象原子服务上下文之间的相互影响,应用回溯算法思想准确有效地将抽象的空间信息服务组合模型转换为工作流引擎可执行的空间信息服务链。     

互联网泛在地理信息感知融合技术综述

互联网数据中蕴含丰富的地理信息,其无处不在、形式与结构多样的特征决定了感知和融合面临许多技术难题。本文在分析互联网泛在地理信息分类和特征的基础上,系统研究其感知和融合技术的总体现状,总结了服务快速准确发现、深层网络数据高覆盖度采集、非结构化文本中位置信息提取和关联图像空间语义提取等感知技术发展现状,分析了异源几何数据匹配关联、地址标准化处理、同名实体语义对齐、地理实体关系构建等融合处理关键技术;在此基础上,总结和展望了互联网泛在地理信息感知融合技术在开放地理数据网络、城市治理与应急管理、网络监测与地理空间情报等领域的应用前景。     

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。