地理空间数据本质特征语义相关度计算模型

关联数据是跨网域整合多源异构地理空间数据的有效方式,语义丰富的关联是准确、快速发现目标数据的关键。根据地理空间数据在空间、时间、内容上的语义关系,提出地理空间数据本质特征语义相关度计算模型。通过构建本质特征的关联指标体系,分层次逐级计算地理空间数据的语义相关度。与传统的语义相关度计算方式不同,以地理元数据为语料库,充分考虑地理空间数据的特点及空间、时间、内容在检索中不同的重要程度,分别采用几何运算、数值运算、词语语义相似度计算和类别层次相关度计算的方式,构建地理空间数据的语义相关度计算模型。该模型具有构建简单、适用于多源异构数据、充分结合了数学运算和专家经验知识等特点。实验表明：模型能够有效地计算地理空间数据本质特征的语义相关度,并具备一定的扩展性。     

Toward an inclusive semantic interoperability: the case of Cree hydrographic features

There has been ample work in GIScience on the formalization of ontologies but a relatively neglected area is the influence of language and culture on ontologies of geography. Although this subject has been investigated for conceptual ontologies using indigenous words denoting geographic features, this article represents the first attempt to develop a logical ontology with an indigenous group. The process of developing logical ontologies is here referred to as formalization. A methodology for formalizing ontologies with indigenous peoples is presented. A conceptual (human readable) ontology and a logical (axioms specified in mathematical logic) ontology were developed using this methodology. Research was conducted with the Cree, the largest indigenous language grouping in Canada. Results show that the geospatial ontology developed from Cree geographic concepts possesses unique design considerations: no superordinate classes were found from archival sources or Cree speakers so ontologies are structurally flat; the ontology contains some unique classes of water bodies; and the ontology challenges our notions of the generalizability of ontologies within indigenous groups. Whereas these difficulties are not insurmountable to the establishment of a cross-cultural Geospatial Semantic Web, the current plans of the World Wide Web Consortium do not adequately address them. We suggest future directions toward an inclusive semantic interoperability.     

地理空间元数据关联网络的构建

利用资源描述框架（RDF）设计地理空间元数据关联模型,根据地理空间元数据之间的语义关系和语义相关度的计算,以构建以元数据为节点、元数据之间的语义关系为边、语义相关度为权重的关联网络。在这一网络中,一个节点是一个地理空间元数据的资源描述图,包含属性特征（数据来源、空间特征、时间特征、内容）及其关系特征（元数据之间的语义关系、语义相关度）。实验及其分析表明,地理空间元数据关联网络可以有效地支持地理空间数据语义关联检索、推荐等应用,这与传统的基于关键词的元数据检索方式相比,具有更高的准确度。     

Framework for probabilistic geospatial ontologies

Partial knowledge about geospatial categories is important for practical use of ontologies in the geospatial domain. Degree of overlaps between geospatial categories, especially those based on geospatial actions concepts and geospatial enitity concepts, need to be specified in ontologies. Conventional geospatial ontologies do not enable specification of such information, and this presents difficulties in ontology reasoning for practical purposes. We present a framework to encode probabilistic information in geospatial ontologies based on the BayesOWL approach. The approach enables rich inferences such as most similar concepts within and across ontologies. This paper presents two case studies of using road‐network ontologies to demonstrate the framework for probabilistic geospatial ontologies. Besides inferences within the probabilistic ontologies, we discuss inferences about most similar concepts across ontologies based on the assumption that geospatial action concepts are invariable. The results of such machine‐based mappings of most similar concepts are verified with mappings of concepts extracted from human subjects testing. The practical uses of probabilistic geospatial ontologies for concept matching and measuring naming heterogeneities between two ontologies are discussed. Based on our experiments, we propose such a framework for probabilistic geospatial ontologies as an advancement of the proposal to develop semantic reference systems.     

考虑空间关系的极地科学数据本体表达与构建方法研究

两极是世界各国关注和研究的热点地区,极地科学数据量飞速增长,如何高效地管理极地科学数据并提供数据的智能服务,已成为极地数据库研究的重点。针对目前以关键词和主题词等字符匹配为核心的数据库查询中存在的查不全、查不准等问题,以中国南北极数据中心的元数据库作为数据源,引入本体论思想,提出了考虑空间关系的极地科学数据本体构建方法,研究了极地科学数据的语义概念、属性及语义关系,着重考虑了空间关系在本体构建中的形式化表示模型;设计了考虑空间关系的极地本体总体框架;最后,利用本体构建工具实现了极地科学数据本体库的搭建。考虑空间关系的极地科学数据本体库的建立对极地科学数据的精确发现和信息共享提供了有力的支撑。     

Barrier dynamics for GIS: a design pattern for geospatial barriers

When separation between geospatial features is desired, an obstacle or barrier is used to maintain distance between the features. Barriers are used in a wide variety of domains, such as hazards, transportation, and public safety. Floodwalls, for example, prevent flooding rivers or lakes from inundating buildings. This work presents an ontology design pattern for barriers and barring events useful for GIS. In this ontology design pattern, active features (e.g., the water-body that is causing flooding) and target features (e.g., buildings requiring flood protection) are defined. Along with these key features, the ontology design pattern also captures certain barring events. These are the dynamics that arise from the placement of barriers and include Blocking, Enclosing, KeepingIn and KeepingOut, involving interactions among barriers, and active and target features. The set of classes that model these features and events as well as the relations between them are presented and formalized. This design pattern is ready for integration in a GIS to allow queries on barriers and barring events that do not currently have explicit support in most GIS.     

黄土地貌本体的构建方法研究

由于人类认知差异和语义描述的模糊性,导致地理要素语义表达的不一致以及信息共享的极大困难.鉴于本体是解决基于语义知识表达的有效方法,该文以黄土地貌为例,使用本体的形式化定义形式,对黄土地貌概念进行语义形式化表达,提出了构建黄土地貌的五元组逻辑结构,进而建立了黄土地貌的本体模型.     

Comparative assessment of semantic-sensitive satellite image retrieval: simple and context-sensitive Bayesian networks

In recent years, Bayesian networks using unsupervised extracted image features have been applied in many remote sensing information mining systems to enable semantic-sensitive image retrieval. However, a simple Bayesian network insufficiently accounts for the spatial information, that is, the relations among image regions, for the semantic inference process. This drawback significantly impacts the retrieval performance, especially if the utilised features contain no or little spatial information. Therefore, this article proposes a context-sensitive Bayesian network, which infers semantic concepts of image regions based on the spectral and textural characteristics of the regions themselves as well as their contexts, that is, the adjacent regions. In order to compare the context-sensitive Bayesian network with the simple Bayesian network, comprehensive experiments were conducted based on high-resolution multispectral IKONOS imagery. The results show that the incorporation of the image regions' spatial relations not only significantly improves the accuracy of the semantic concepts inference, but also allows more flexibility in choosing the type of low-level features.     

A comprehensive methodology for discovering semantic relationships among geospatial vocabularies using oceanographic data discovery as an example

It is challenging to find relevant data for research and development purposes in the geospatial big data era. One long-standing problem in data discovery is locating, assimilating and utilizing the semantic context for a given query. Most research in the geospatial domain has approached this problem in one of two ways: building a domain-specific ontology manually or discovering automatically, semantic relationships using metadata and machine learning techniques. The former relies on rich expert knowledge but is static, costly and labor intensive, whereas the second is automatic and prone to noise. An emerging trend in information science takes advantage of large-scale user search histories, which are dynamic but subject to user- and crawler-generated noise. Leveraging the benefits of these three approaches and avoiding their weaknesses, a novel methodology is proposed to (1) discover vocabulary-based semantic relationships from user search histories and clickstreams, (2) refine the similarity calculation methods from existing ontologies and (3) integrate the results of ontology, metadata, user search history and clickstream analysis to better determine their semantic relationships. An accuracy assessment by domain experts for the similarity values indicates an 83% overall accuracy for the top 10 related terms over randomly selected sample queries. This research functions as an example for building vocabulary-based semantic relationships for different geographical domains to improve various aspects of data discovery, including the accuracy of the vocabulary relationships of commonly used search terms.     

Geospatial Thinking and Spatial Ability: An Empirical Examination of Knowledge and Reasoning in Geographical Science

This study examined the relationships between geospatial thinking and spatial ability, focusing on knowledge and reasoning about fundamental concepts in geospatial science. Scores on a geospatial thinking test had low correlations with each other, compared to high correlations among scores on spatial ability tests, and the degrees of relationship between geospatial thinking and spatial ability depended on the type of geospatial thinking. Expert geospatial scientists did not outperform students on spatial ability tests, suggesting that expertise in geospatial science might not simply be explained by superiority in spatial ability. The results point to the necessity of targeted instruction for geospatial learning.     

An evaluation of ontology matching techniques on geospatial ontologies

Standardization is one of the pillars of interoperability. In this context, efforts promoted by the Open Geospatial Consortium, such as CityGML (Technical University, Berlin), a standard for exchanging three-dimensional models or urban city objects, are welcomed. However, information from other domains of interest (e.g. energy efficiency or building information modeling) is needed for tasks such as land planning, large-scale flooding analysis, or demand/supply energy simulations. CityGML allows extension in order to integrate information from other domains, but the development process is expensive because there is no way to perform it automatically. The discovery of correspondences between CityGML concepts and other domains concepts poses a significant challenge.

Ontology matching is the research field emerged from the Semantic Web to address automatic ontology integration. Using the ontology underlying CityGML and the ontologies which model other domains of interest, ontology matching would be able to find the correspondences that would permit the integration in a more automatic manner than it is done now.

In this paper, we evaluate if ontology matching techniques allow performing an automatic integration of geospatial information modeled from different viewpoints. In order to achieve this, an evaluation methodology was designed, and it was applied to the discovery of relationships between CityGML and ontologies coming from the building information modeling and Geospatial Semantic Web domains. The methodology and the results of the evaluation are presented. The best results have been achieved using string-based techniques, while matching systems give the worst precision and recall. Only in a few cases the values are over 50%, which shows the limitations when these techniques are applied to ontologies with a partial overlap.     

Extracting and analyzing semantic relatedness between cities using news articles

News articles capture a variety of topics about our society. They reflect not only the socioeconomic activities that happened in our physical world, but also some of the cultures, human interests, and public concerns that exist only in the perceptions of people. Cities are frequently mentioned in news articles, and two or more cities may co-occur in the same article. Such co-occurrence often suggests certain relatedness between the mentioned cities, and the relatedness may be under different topics depending on the contents of the news articles. We consider the relatedness under different topics as semantic relatedness. By reading news articles, one can grasp the general semantic relatedness between cities; yet, given hundreds of thousands of news articles, it is very difficult, if not impossible, for anyone to manually read them. This paper proposes a computational framework which can ‘read’ a large number of news articles and extract the semantic relatedness between cities. This framework is based on a natural language processing model and employs a machine learning process to identify the main topics of news articles. We describe the overall structure of this framework and its individual modules, and then apply it to an experimental dataset with more than 500,000 news articles covering the top 100 US cities spanning a 10-year period. We perform exploratory visualizations of the extracted semantic relatedness under different topics and over multiple years. We also analyze the impact of geographic distance on semantic relatedness and find varied distance decay effects. The proposed framework can be used to support large-scale content analysis in city network research.     

Characterizing and representing inference histories in geologic mapping

Evaluation of the utility of a geologic map can require understanding of its knowledge evolution. In such cases, online usage further requires machine representation of both the knowledge and the evolution. To meet these needs, an informal structure is developed here that involves the interaction of the three reasoning forms of abduction, induction, and deduction. Empirical results are presented that support the structure, and a foundational ontology for science knowledge is adapted to represent the structure. These results lead to a new understanding of the role of the three reasoning forms in geologic mapping and constitute a new approach to the machine representation of geologic knowledge evolution. The approach also has potential to characterize the mapping of many types of geographic regions.     

Assessing similarity matching for possible integration of feature classifications of geospatial data from official and informal sources

One difficulty in integrating geospatial data sets from different sources is variation in feature classification and semantic content of the data. One step towards achieving beneficial semantic interoperability is to assess the semantic similarity among objects that are categorised within data sets. This article focuses on measuring semantic and structural similarities between categories of formal data, such as Ordnance Survey (OS) cartographic data, and volunteered geographic information (VGI), such as that sourced from OpenStreetMap (OSM), with the intention of assessing possible integration. The model involves ‘tokenisation’ to search for common roots of words, and the feature classifications have been modelled as an XML schema labelled rooted tree for hierarchical analysis. The semantic similarity was measured using the WordNet::Similarity package, while the structural similarities between sub-trees of the source and target schemas have also been considered. Along with dictionary and structural matching, the data type of the category itself is a comparison variable. The overall similarity is based on a weighted combination of these three measures. The results reveal that the use of a generic similarity matching system leads to poor agreement between the semantics of OS and OSM data sets. It is concluded that a more rigorous peer-to-peer assessment of VGI data, increasing numbers and transparency of contributors, the initiation of more programs of quality testing and the development of more directed ontologies can improve spatial data integration.     

基于主体人与地学本体的人-地-GIS关系讨论

“人-地-GIS”关系是地理信息科学及相关研究的基础。传统的人-地-GIS双向联系模型正在向以地学本体为核心的本体中心模型演化。通过主体人与客体人的区分,探讨两者在“人-地-GIS”关系中的地位与作用,分析人-地、地-GIS与人-GIS的相关关系。通过地学本体与本体论的分析,讨论地学本体与科学本体、哲学本体论之间的关系,分析地学本体与类、元数据之间的区别与联系。进而构建以地学本体为核心的“人-地-GIS”关系新模型,该模型整体上分为认知与再现两部分,由核心的本体模型及人的认知层、GIS的模型层与“地学”现象及其关系层构成。以主体人的认知为主线,讨论该模型中各模块的功能与相互关系,指出今后的发展方向。