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Reza Kalbasi Krzysztof Janowicz Femke Reitsma Luc Boerboom Ali Alesheikh 《Transactions in GIS》2014,18(6):834-851
Ontology‐based information publishing, retrieval, reuse, and integration have become popular research topics to address the challenges involved in exchanging data between heterogeneous sources. However, in most cases ontologies are still developed in a centralized top‐down manner by a few knowledge engineers. Consequently, the role that developers play in conceptualizing a domain such as the geosciences is disproportional compared with the role of domain experts and especially potential end‐users. These and other drawbacks have stimulated the creation of new methodologies focusing around collaboration. Based on a review of existing approaches, this article presents a two‐step methodology and implementation to foster collaborative ontology engineering in the geosciences. Our approach consists of the development of a minimalistic core ontology acting as a catalyst and the creation of a virtual collaborative development cycle. Both methodology and prototypical implementation have been tested in the context of the EU‐funded ForeStClim project which addresses environmental protection with respect to forests and climate change. 相似文献
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With the rapid advance of geospatial technologies, the availability of geospatial data from a wide variety of sources has increased dramatically. It is beneficial to integrate / conflate these multi‐source geospatial datasets, since the integration of multi‐source geospatial data can provide insights and capabilities not possible with individual datasets. However, multi‐source datasets over the same geographical area are often disparate. Accurately integrating geospatial data from different sources is a challenging task. Among the subtasks of integration/conflation, the most crucial one is feature matching, which identifies the features from different datasets as presentations of the same real‐world geographic entity. In this article we present a new relaxation‐based point feature matching approach to match the road intersections from two GIS vector road datasets. The relaxation labeling algorithm utilizes iterated local context updates to achieve a globally consistent result. The contextual constraints (relative distances between points) are incorporated into the compatibility function employed in each iteration's updates. The point‐to‐point matching confidence matrix is initialized using the road connectivity information at each point. Both the traditional proximity‐based approach and our relaxation‐based point matching approach are implemented and experiments are conducted over 18 test sites in rural and suburban areas of Columbia, MO. The test results show that our relaxation labeling approach has much better performance than the proximity matching approach in both simple and complex situations. 相似文献
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Krzysztof Janowicz 《Transactions in GIS》2012,16(3):351-374
Big Data, Linked Data, Smart Dust, Digital Earth, and e‐Science are just some of the names for research trends that surfaced over the last years. While all of them address different visions and needs, they share a common theme: How do we manage massive amounts of heterogeneous data, derive knowledge out of them instead of drowning in information, and how do we make our findings reproducible and reusable by others? In a network of knowledge, topics span across scientific disciplines and the idea of domain ontologies as common agreements seems like an illusion. In this work, we argue that these trends require a radical paradigm shift in ontology engineering away from a small number of authoritative, global ontologies developed top‐down, to a high number of local ontologies that are driven by application needs and developed bottom‐up out of observation data. Similarly as the early Web was replaced by a social Web in which volunteers produce data instead of purely consuming it, the next generation of knowledge infrastructures has to enable users to become knowledge engineers themselves. Surprisingly, existing ontology engineering frameworks are not well suited for this new perspective. Hence, we propose an observation‐driven ontology engineering framework, show how its layers can be realized using specific methodologies, and relate the framework to existing work on geo‐ontologies. 相似文献
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Geospatial Ontology Development and Semantic Analytics 总被引：3，自引：0，他引：3
I Budak Arpinar Amit Sheth Cartic Ramakrishnan E Lynn Usery Molly Azami Mei-Po Kwan 《Transactions in GIS》2006,10(4):551-575
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. 相似文献
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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. 相似文献
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This paper presents ongoing research in the field of extensional mappings between ontologies. Hitherto, the task of generating mappings between ontologies has focused on the intensional level of ontologies. The term intensional level herein, refers to the set of concepts that are included in an ontology. However, an ontology that has been created for a specific task or application needs to be populated with instances. These comprise the extensional level of an ontology. This particular level is generally neglected during the ontologies’ integration procedure. Thus, although methodologies of geographic ontologies integration, ranging from alignment to true integration, have, in the course of years, presented a solid ground for information exchange, little has been done in exploring the relationships between the data. In this context, this research strives to set a framework for extensional mappings between ontologies using Information Flow Theory by presenting a case study of interoperability between the thematic content of maps. 相似文献
8.
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。 相似文献
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Using Ontologies for Integrated Geographic Information Systems 总被引：13，自引：0，他引：13
Frederico T. Fonseca Max J. Egenhofer Peggy Agouris & Gilberto Câmara 《Transactions in GIS》2002,6(3):231-257
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. 相似文献
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《International Journal of Digital Earth》2013,6(3):259-278
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. 相似文献
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Miriam Baglioni Emiliano Giovannetti Maria Vittoria Masserotti Chiara Renso Laura Spinsanti 《Transactions in GIS》2008,12(Z1):31-44
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. 相似文献
13.
In information systems, ontologies promise advantages such as enhanced interoperability, knowledge sharing, and integration of data sources. In this article, we show that the upper ontology Basic Formal Ontology can facilitate the modeling of an evolution of administrative units. This is demonstrated by creating a spatiotemporal ontology for the administrative units of Switzerland. The ontology tackles the problem that the geometric data is typically captured by taking snapshots at regular intervals while the thematic data is continually updated. The ontology presented merges time‐stamped geometries with a formally described history of administrative units, allowing for complex spatiotemporal queries neither standard approach would support. The resulting populated knowledge base was evaluated against a set of spatiotemporal test queries. The evaluation showed that this knowledge base supports a wide range of queries on the evolution of the administrative units of Switzerland between 1960 and 2010. 相似文献
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Didier G. Leibovici Lucy Bastin Suchith Anand Gobe Hobona Mike Jackson 《Transactions in GIS》2011,15(3):347-364
Overlaying maps using a desktop GIS is often the first step of a multivariate spatial analysis. The potential of this operation has increased considerably as data sources and Web services to manipulate them are becoming widely available via the Internet. Standards from the OGC enable such geospatial ‘mashups’ to be seamless and user driven, involving discovery of thematic data. The user is naturally inclined to look for spatial clusters and ‘correlation’ of outcomes. Using classical cluster detection scan methods to identify multivariate associations can be problematic in this context, because of a lack of control on or knowledge about background populations. For public health and epidemiological mapping, this limiting factor can be critical but often the focus is on spatial identification of risk factors associated with health or clinical status. In this article we point out that this association itself can ensure some control on underlying populations, and develop an exploratory scan statistic framework for multivariate associations. Inference using statistical map methodologies can be used to test the clustered associations. The approach is illustrated with a hypothetical data example and an epidemiological study on community MRSA. Scenarios of potential use for online mashups are introduced but full implementation is left for further research. 相似文献
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《制图学和地理信息科学》2013,40(1):13-28
A research agenda is presented which addresses the current role and potential of map displays. By considering the geospatial data used in visualization, the form and design of maps, the purposes for which map displays are created, the nature of the map user community, and the technology employed to visualize geospatial data, a thorough overview of the nature of cartographic visualization is given. Under the same themes, and sourced in cartographic tradition, cartographic practice and technological opportunities, a series of possible research avenues are highlighted. The important links between representation and the user interface, map user cognition and the geospatial database are stressed. 相似文献
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Although explainable artificial intelligence (XAI) promises considerable progress in glassboxing deep learning models, there are challenges in applying XAI to geospatial artificial intelligence (GeoAI), specifically geospatial deep neural networks (DNNs). We summarize these as three major challenges, related generally to XAI computation, to GeoAI and geographic data handling, and to geosocial issues. XAI computation includes the difficulty of selecting reference data/models and the shortcomings of attributing explanatory power to gradients, as well as the difficulty in accommodating geographic scale, geovisualization, and underlying geographic data structures. Geosocial challenges encompass the limitations of knowledge scope—semantics and ontologies—in the explanation of GeoAI as well as the lack of integrating non-technical aspects in XAI, including processes that are not amenable to XAI. We illustrate these issues with a land use classification case study. 相似文献
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Boriana Deliiska 《Transactions in GIS》2007,11(4):637-651
Geoinformatics is a comparatively new interdisciplinary science and as a part of space informatics uses methods and terminology of informatics and many natural sciences. An ontology of geoinformatics is discussed in the paper, especially concerning its structure, relationships with other ontologies, resources for development and utilization. The ontology of geoinformatics is a kind of domain ontology and has a layered structure consisting of syntactic and semantic layers. The corpus of this ontology is an existing multilingual dictionary of geographical information systems (GIS) enriched with terminology from other external sources. The building of the ontology is preceded by the development of a taxonomy and thesaurus of geoinformatics. The thesaurus database is converted into an OWL ontology by a Visual Basic application. The reusing of the ontology is proposed by its transformation in application ontologies for geoinformatics education. 相似文献
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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. 相似文献
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《International Journal of Digital Earth》2013,6(5):383-402
Geospatial Semantic Web promises better retrieval geospatial information for Digital Earth systems by explicitly representing the semantics of data through ontologies. It also promotes sharing and reuse of geospatial data by encoding it in Semantic Web languages, such as RDF, to form geospatial knowledge base. For many applications, rapid retrieval of spatial data from the knowledge base is critical. However, spatial data retrieval using the standard Semantic Web query language – Geo-SPARQL – can be very inefficient because the data in the knowledge base are no longer indexed to support efficient spatial queries. While recent research has been devoted to improving query performance on general knowledge base, it is still challenging to support efficient query of the spatial data with complex topological relationships. This research introduces a query strategy to improve the query performance of geospatial knowledge base by creating spatial indexing on-the-fly to prune the search space for spatial queries and by parallelizing the spatial join computations within the queries. We focus on improving the performance of Geo-SPARQL queries on knowledge bases encoded in RDF. Our initial experiments show that the proposed strategy can greatly reduce the runtime costs of Geo-SPARQL query through on-the-fly spatial indexing and parallel execution. 相似文献