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1.
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
2.
Naicong Li Robert Raskin Michael Goodchild Krzysztof Janowicz 《Transactions in GIS》2012,16(3):313-329
Numerous systems and tools have been developed for spatial decision support (SDS), but they generally suffer from a lack of re‐usability, inconsistent terminology, and weak conceptualization. We introduce a collaborative effort by the SDS Consortium to build a SDS knowledge portal. We present the formal representation of knowledge about SDS, the various ontologies captured and made accessible by the portal, and the processes used to create them. We describe the portal in action, and the ways in which users can search, browse, and make use of its content. Finally, we discuss the lessons learned from this effort, and future development directions. Our work demonstrates how ontologies and semantic technologies can support the documentation and retrieval of dynamic knowledge in GIScience by offering flexible schemata instead of fixed data structures. 相似文献
3.
Definitions of categories in existent geospatial ontologies are an invaluable source of information because they provide us with essential knowledge about concepts and their properties. A closer examination reveals that definitions also contain supplementary linguistic items, which are mainly qualitative expressions, such as quantifiers. This inclusion of modifiers in definitions affects the way values are assigned to the categories’ properties (semantic properties and relations). This paper introduces a methodology for: (1) representing the essence of qualitative information to clarify the identity relations among categories; and (2) assessing their semantic similarity in order to disambiguate the taxonomic structure of existent geospatial ontologies. 相似文献
4.
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
5.
Geo-ontology Tools: The Missing Link 总被引：1，自引：0，他引：1
Numerous authors have presented ontology building tools that have all been developed as part of academic projects and that are usually adaptations of more generic tools for geo-spatial applications. While we trust that these tools do their job for the special purpose they have been built, the GIScience user community is still a long way away from off-the-shelf ontology builders that can be used by GIS project managers. In this article, we present a comparative study of ontology building tools described in some twenty peer-reviewed GIScience journal articles. We analyze them from the perspective of two application domains, crime analysis and transportation/land use. For the latter, we developed a database schema, which is substantially different from the three main templates commonly used. The crime analysis application uses a rule base for an agent-based model that had no precursor. In both cases, the currently available set of tools cannot replace manual coding of ontologies for use with ESRI-based application software. Based on these experiences, we outline a requirements list of what the tools described in the first part of the article are missing to make them practical from an applications perspective. The result is an R&D agenda for this important aspect of GIScience. 相似文献
6.
The realization that knowledge often forms a densely interconnected graph has fueled the development of graph databases, Web‐scale knowledge graphs and query languages for them, novel visualization and query paradigms, as well as new machine learning methods tailored to graphs as data structures. One such example is the densely connected and global Linked Data cloud that contains billions of statements about numerous domains, including life science and geography. While Linked Data has found its way into everyday applications such as search engines and question answering systems, there is a growing disconnect between the classical ways in which Geographic Information Systems (GIS) are still used today and the open‐ended, exploratory approaches used to retrieve and consume data from knowledge graphs such as Linked Data. In this work, we conceptualize and prototypically implement a Linked Data connector framework as a set of toolboxes for Esri's ArcGIS to close this gap and enable the retrieval, integration, and analysis of Linked Data from within GIS. We discuss how to connect to Linked Data endpoints, how to use ontologies to probe data and derive appropriate GIS representations on the fly, how to make use of reasoning, how to derive data that are ready for spatial analysis out of RDF triples, and, most importantly, how to utilize the link structure of Linked Data to enable analysis. The proposed Linked Data connector framework can also be regarded as the first step toward a guided geographic question answering system over geographic knowledge graphs. 相似文献
7.
Lucas May Petry Carlos Andres Ferrero Luis Otavio Alvares Chiara Renso Vania Bogorny 《Transactions in GIS》2019,23(5):960-975
The large amount of semantically rich mobility data becoming available in the era of big data has led to a need for new trajectory similarity measures. In the context of multiple‐aspect trajectories, where mobility data are enriched with several semantic dimensions, current state‐of‐the‐art approaches present some limitations concerning the relationships between attributes and their semantics. Existing works are either too strict, requiring a match on all attributes, or too flexible, considering all attributes as independent. In this article we propose MUITAS, a novel similarity measure for a new type of trajectory data with heterogeneous semantic dimensions, which takes into account the semantic relationship between attributes, thus filling the gap of the current trajectory similarity methods. We evaluate MUITAS over two real datasets of multiple‐aspect social media and GPS trajectories. With precision at recall and clustering techniques, we show that MUITAS is the most robust measure for multiple‐aspect trajectories. 相似文献
8.
ABSTRACT Geovisualisation is a knowledge-intensive art in which both providers and users need to possess a wide range of knowledge. Current syntactic approaches to presenting visualisation information lack semantics on the one hand, and on the other hand are too bespoke. Such limitations impede the transfer, interpretation, and reuse of the geovisualisation knowledge. In this paper, we propose a knowledge-based approach to formally represent geovisualisation knowledge in a semantically-enriched and machine-readable manner using Semantic Web technologies. Specifically, we represent knowledge regarding cartographic scale, data portrayal and geometry source, which are three key aspects of geovisualisation in the contemporary web mapping era, coupling ontologies and semantic rules. The knowledge base enables inference for deriving the corresponding geometries and portrayals for visualisation under different conditions. A prototype system is developed in which geospatial linked data are used as underlying data, and some geovisualisation knowledge is formalised into a knowledge base to visualise the data and provide rich semantics to users. The proposed approach can partially form the foundation for the vision of web of knowledge for geovisualisation. 相似文献
9.
李欣 《测绘与空间地理信息》2008,31(4)
数据挖掘系统是通过在一个软件平台上有效地集成一种或若干种数据挖掘算法模型,并结合相应的数据源,以完成特定的挖掘应用或通用的挖掘任务,提取对用户有用的模式、规则、知识的信息系统.本文实现了名为SDMiner的空间数据可视化挖掘系统,把传统挖掘系统的体系结构与面向服务的体系结构相结合,提出了一个新的面向服务的数据挖掘系统体系结构.系统能够面向Web用户提供分布式的挖掘服务,以任务的定义为核心组织数据挖掘流程,能够动态添加各种算法.以适应各种数据处理的要求. 相似文献
10.
Wendy R. Eisner Jessica Jelacic Chris J. Cuomo Changjoo Kim Kenneth M. Hinkel Dorin Del Alba 《Transactions in GIS》2012,16(1):17-37
A traditional knowledge “Iñupiaq Web GIS”, based on a five‐year study and containing observations and environmental knowledge of Iñupiat communities indigenous to Arctic Alaska, was incorporated into a Web‐based platform. The website, “Arctic Cultural Cartography,” was created to be an open portal through which the password‐protected “Iñupiaq Web GIS” could be accessed. We discuss the process of developing the web GIS including the incorporation of user‐friendly features such as links to interactive maps, video clips of interviews, discussion boards, and the integration of popular web interfaces such as Facebook. We also discuss short‐ and long‐term goals for the further development of the GIS, its potential as a sustainable, participatory online database for sharing pertinent ecological knowledge, and challenges in achieving optimal community involvement given constraints imposed by remote locations with limited bandwidth. 相似文献
11.
Spatial signatures for geographic feature types: examining gazetteer ontologies using spatial statistics 总被引：1，自引：0，他引：1 
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下载免费PDF全文 Digital gazetteers play a key role in modern information systems and infrastructures. They facilitate (spatial) search, deliver contextual information to recommended systems, enrich textual information with geographical references, and provide stable identifiers to interlink actors, events, and objects by the places they interact with. Hence, it is unsurprising that gazetteers, such as GeoNames, are among the most densely interlinked hubs on the Web of Linked Data. A wide variety of digital gazetteers have been developed over the years to serve different communities and needs. These gazetteers differ in their overall coverage, underlying data sources, provided functionality, and geographic feature type ontologies. Consequently, place types that share a common name may differ substantially between gazetteers, whereas types labeled differently may, in fact, specify the same or similar places. This makes data integration and federated queries challenging, if not impossible. To further complicate the situation, most popular and widely adopted geo‐ontologies are lightweight and thus under‐specific to a degree where their alignment and matching become nothing more than educated guesses. The most promising approach to addressing this problem, and thereby enabling the meaningful integration of gazetteer data across feature types, seems to be a combination of top‐down knowledge representation with bottom‐up data‐driven techniques such as feature engineering and machine learning. In this work, we propose to derive indicative spatial signatures for geographic feature types by using spatial statistics. We discuss how to create such signatures by feature engineering and demonstrate how the signatures can be applied to better understand the differences and commonalities of three major gazetteers, namely DBpedia Places, GeoNames, and TGN. 相似文献
12.
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
13.
基于描述逻辑本体的GIS多重表达 总被引：8，自引：1，他引：7
多重表达是地理信息弹性表达和弹性存取的一个内在要求,它实质上提供了一个多尺度、多应用主题的数据集成机制。多重表达的建模不能仅限于数据库中多重几何特征的一致性表达,还必须支持不同语义粒度、不同应用主题下语义特征的弹性描述。基于形式化本体的地理信息建模更贴近于认知模型,还有助于语义表达以及基于语义的信息集成和共享。通过经典描述逻辑中具体域以及context的扩展,满足区域拓扑和上下文语义形式化描述的需要;给出一个基于描述逻辑的多表达地理本体方案,该方案能够为数据库中几何信息和语义信息的弹性表达提供一个统一的基于逻辑的模型理论基础。 相似文献
14.
This article presents a methodology for designing a WebGIS framework intended for automatically analyzing spatial data and updating statistics of interest with new information inserted daily by multiple users via a Web portal. A practical example is used on vehicle accident data for assessing risk in specific road segments. Two main blocks integrated together will be described: the collaborative block and the data‐analysis block. The former gives end‐users computer‐aided tools to view, insert, modify and manage data related to accidents and traffic monitoring sensors, whereas the latter is developed to automatically analyze the accident data coming from user's collaboration. Because different agencies can survey accident sites, a collaborative environment is necessary – and a Web‐based solution is ideal – for permitting multi‐user access and data insertion. A centralized approach to process the data in real time is described in all its components. Server‐side Structured Query Language functions optimize performance by using dedicated libraries for spatial processing and re‐structuring the attributes associated with elements which are consequently re‐classified for correct color‐scaling. The end‐product is a system that provides a seamless integration of front‐end tools for user collaboration and back‐end tools to update accident risk statistics in real time and provide them to stakeholders. 相似文献
15.
ADCN: An anisotropic density‐based clustering algorithm for discovering spatial point patterns with noise 下载免费PDF全文
下载免费PDF全文 Density‐based clustering algorithms such as DBSCAN have been widely used for spatial knowledge discovery as they offer several key advantages compared with other clustering algorithms. They can discover clusters with arbitrary shapes, are robust to noise, and do not require prior knowledge (or estimation) of the number of clusters. The idea of using a scan circle centered at each point with a search radius Eps to find at least MinPts points as a criterion for deriving local density is easily understandable and sufficient for exploring isotropic spatial point patterns. However, there are many cases that cannot be adequately captured this way, particularly if they involve linear features or shapes with a continuously changing density, such as a spiral. In such cases, DBSCAN tends to either create an increasing number of small clusters or add noise points into large clusters. Therefore, in this article, we propose a novel anisotropic density‐based clustering algorithm (ADCN). To motivate our work, we introduce synthetic and real‐world cases that cannot be handled sufficiently by DBSCAN (or OPTICS). We then present our clustering algorithm and test it with a wide range of cases. We demonstrate that our algorithm can perform equally as well as DBSCAN in cases that do not benefit explicitly from an anisotropic perspective, and that it outperforms DBSCAN in cases that do. Finally, we show that our approach has the same time complexity as DBSCAN and OPTICS, namely O(n log n) when using a spatial index and O(n2) otherwise. We provide an implementation and test the runtime over multiple cases. 相似文献
16.
Yao Yao Penghua Liu Ye Hong Zhaotang Liang Rouyu Wang Qingfeng Guan Jingmin Chen 《Transactions in GIS》2019,23(5):1029-1047
The rapid development of urban retail companies brings new opportunities to the Chinese economy. Due to the spatiotemporal heterogeneity of different cities, selecting a business location in a new area has become a challenge. The application of multi‐source geospatial data makes it possible to describe human activities and urban functional zones at fine scale. We propose a knowledge transfer‐based model named KTSR to support citywide business location selections at the land‐parcel scale. This framework can optimize customer scores and study the pattern of business location selection for chain brands. First, we extract the features of each urban land parcel and study the similarities between them. Then, singular value decomposition was used to build a knowledge‐transfer model of similar urban land parcels between different cities. The results show that: (1) compared with the actual scores, the estimated deviation of the proposed model decreased by more than 50%, and the Pearson correlation coefficient reached 0.84 or higher; (2) the decomposed features were good at quantifying and describing high‐level commercial operation information, which has a strong relationship with urban functional structures. In general, our method can work for selecting business locations and estimating sale volumes and user evaluations. 相似文献
17.
Over the past decade, the electric vehicle (EV) industry has experienced unprecedented growth and diversification, resulting in a complex ecosystem. To effectively manage this multifaceted field, we present an EV-centric knowledge graph (EVKG) as a comprehensive, cross-domain, extensible, and open geospatial knowledge management system. The EVKG encapsulates essential EV-related knowledge, including EV adoption, EV supply equipment, and electricity transmission network, to support decision-making related to EV technology development, infrastructure planning, and policy-making by providing timely and accurate information and analysis. To enrich and contextualize the EVKG, we integrate the developed EV-relevant ontology modules from existing well-known knowledge graphs and ontologies. This integration enables interoperability with other knowledge graphs in the Linked Data Open Cloud, enhancing the EVKG's value as a knowledge hub for EV decision-making. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV-related questions, providing critical insights into the EV ecosystem. Our EVKG provides an efficient and effective approach for managing the complex and diverse EV industry. By consolidating critical EV-related knowledge into a single, easily accessible resource, the EVKG supports decision-makers in making informed choices about EV technology development, infrastructure planning, and policy-making. As a flexible and extensible platform, the EVKG is capable of accommodating a wide range of data sources, enabling it to evolve alongside the rapidly changing EV landscape. 相似文献
18.
A rich amount of geographic information exists in unstructured texts, such as web pages, social media posts, housing advertisements, and historical archives. Geoparsers are useful tools that extract structured geographic information from unstructured texts, thereby enabling spatial analysis on textual data. While a number of geoparsers have been developed, they have been tested on different data sets using different metrics. Consequently, it is difficult to compare existing geoparsers or to compare a new geoparser with existing ones. In recent years, researchers have created open and annotated corpora for testing geoparsers. While these corpora are extremely valuable, much effort is still needed for a researcher to prepare these data sets and deploy geoparsers for comparative experiments. This article presents EUPEG: an Extensible and Unified Platform for Evaluating Geoparsers. EUPEG is an open source and web‐based benchmarking platform which hosts the majority of open corpora, geoparsers, and performance metrics reported in the literature. It enables direct comparison of the geoparsers hosted, and a new geoparser can be connected to EUPEG and compared with other geoparsers. The main objective of EUPEG is to reduce the time and effort that researchers have to spend in preparing data sets and baselines, thereby increasing the efficiency and effectiveness of comparative experiments. 相似文献
19.
Damien Arvor Mariana Belgiu Zoe Falomir Isabelle Mougenot Laurent Durieux 《地理信息系统科学与遥感》2019,56(6):911-939
The development of new sensors and easier access to remote sensing data are significantly transforming both the theory and practice of remote sensing. Although data-driven approaches based on innovative algorithms and enhanced computing capacities are gaining importance to process big Earth Observation data, the development of knowledge-driven approaches is still considered by the remote sensing community to be one of the most important directions of their research. In this context, the future of remote sensing science should be supported by knowledge representation techniques such as ontologies. However, ontology-based remote sensing applications still have difficulty capturing the attention of remote sensing experts. This is mainly because of the gap between remote sensing experts’ expectations of ontologies and their real possible contribution to remote sensing. This paper provides insights to help reduce this gap. To this end, the conceptual limitations of the knowledge-driven approaches currently used in remote sensing science are clarified first. Then, the different modes of definition of geographic concepts, their duality, vagueness and ambiguity, and the sensory and semantic gaps are discussed in order to explain why ontologies can help address these limitations. In particular, this paper focuses on the capacity of ontologies to represent both symbolic and numeric knowledge, to reason based on cognitive semantics and to share knowledge on the interpretation of remote sensing images. Finally, a few recommendations are provided for remote sensing experts to comprehend the advantages of ontologies in interpreting satellite images. 相似文献