Geographic Ontology Research Articles

Map retrieval intention recognition based on relevance feedback and geographic semantic guidance: For better understanding user retrieval demands

Effective retrieval is essential for finding resources in demand handily amidst extensive data records in data warehouse. Mainstream map retrieval methods suffer from intention gap problem and are incapable to describe sophisticated user demands precisely due to the limits of low- and middle-level text or visual feature matching, resulting in unsatisfactory retrieval results. Such limitations are more marked when map retrieval demands were characterized with joint constraints of geographic concepts. To address this issue, we propose a map retrieval intention recognition method to perceive user demands with relevance feedback samples and geographic semantics guidance. Specifically, we construct a hierarchical intention expression model to describe retrieval goals and their multi-dimensional attribute constrains; incorporate geographic ontologies to provide semantic guidance and facilitate recognition; utilize the frequent itemset mining (FIM) algorithm Apriori to generate intention candidates from relevance feedback samples, and search for the optimal intention set by adopting the minimum description length (MDL) principle. The experiments verify the effectiveness of Apriori algorithm and MDL principle on intention recognition. The proposed method outperforms the FIM algorithm Gene Ontology (RuleGO) and the Decision Tree algorithm with Hierarchical Features (DTHF) with higher recognition accuracy and noise tolerance. Furthermore, through our sample augmentation strategy, the method yields promising recognition accuracy even when the feedback sample size is as low as ten, substantially reducing the feedback burden in human-computer interactions. We envision that the application of our method in spatial data infrastructures (SDIs), such as geoportals and catalogue services, could enhance the quality of service and user experience in geospatial data discovery.

Opening up New Geographical Ontologies around Adapting to Climate Change.

Opening up regional ontologies for climate action is a necessary and underexplored dimension of climate change policymaking. This commentary explores how a regional lens might be integrated into the complex mosaic of climate governance, particularly in the context of resilient regions. I argue regional ontologies for climate policymaking could have greater analytical power if integrated into a theoretical framing of action that goes beyond the nation-state, beyond formal policy processes and beyond a strict binary between science and policy. Applying this lens to resilient regions, I argue there are particular opportunities at the regional scale for highlighting diverse perspectives or adaptation issues obscured through a national ontology, using existing transnational data infrastructure and community-led data systems to support the regional ontology and reframing the scale of collective future visions for a climate-adapted world.

Juggling Complexities

AbstractThe three different engagements with my geographical ontology proposition each produce equally relevant additions to the argument. There are interesting discussions of data needs, geographical synthesis and confronting policy differences. Their common thread is recognition of the inherent complexities facing climate policy making.

Regional geographies of climate change

ABSTRACTThis essay is a commentary on Peter Taylor's article ‘The geographical ontology challenge in attending to anthropogenic climate change: regional geography revisited’. Taylor's article develops, in the context climate change, several themes that he has outlined during his long career. This commentary focuses particularly on the contested (discursive) regional, regional geographical and state‐centric frames critically examined in Taylor's paper. Despite the mushrooming literature on climate change, ontological issues related to regionality/spatialities explored by Taylor have been largely overlooked. He introduces a useful multi‐scalar and multi‐dimensional framework for problematizing the ontologies of the spatialities related to climate change emergency. This commentary focuses on this framework in the context of regional geography. Since regional/territorial, particularly state‐centric frames are frequently taken for granted, Taylor's proposal is very welcome and provides a valuable addition not only to the debate on climate change but also to the ongoing resurgence of regional geography and regional thinking.

The geographical ontology challenge in attending to anthropogenic climate change: regional geography revisited

AbstractAnthropogenic climate change is a complex process that does not respect political boundaries. Thus it is argued states are problematic agencies for tackling the global climate emergency. But it is the world political map that provides the geographical ontology foundation of the massive efforts of climate policy development. Geography's long tradition of regional study is suggested as a means of countering focus on states for policy development. Ontological inventions are proposed that transcend states. These take the form of experimenting with geographical regions encompassing human‐environmental interactions as alternative spatial policy framings to the world political map. Three examples are presented: intergovernmental resilient regions for mitigation; localization through urban sustainable regions; and regions for planetary stewardship of humans‐in‐nature. None of these are ‘solutions’, rather they are illustrations of possible future regional geographies intended to stimulate current cohorts of geographers to contribute necessary regional thinking to the scholarship unpinning climate change policymaking.

On the topographic entity-oriented digital elevation model construction method for urban area land surface

Human activity transforms a land surface into a complex surface where artificial and natural landforms coexist and continuous and emergent landforms merge. In this background, the problems of conventional digital elevation models (DEMs), such as morphological distortion, complicated updates, and lack of information, are increasingly prominent. This study proposes a new idea of DEM construction based on the concept of geographic ontology. First, landforms with common features are abstracted into a certain type of topographic entity based on their morphologies and semantics. For each type of topographic entity, a DEM was constructed independently based on the available elevation information and other information about the semantics and spatial relationships. Second, individual DEMs were merged into a complete DEM following certain rules. A 1 km2 area located in the suburb of Nanjing, Jiangsu Province, China, was selected as the experimental area. The effectiveness of the model construction method proposed in this study was verified. The results show that the DEM constructed according to the idea of this study has a significantly better performance than the conventional DEMs. The constructed DEM in this study can well represent ground objects, such as slopes, farmland, and ditches. In particular, the constructed DEM ensures the morphological accuracy of the ground objects.

Construction of Knowledge Graph Based on Geographic Ontology

Here are massive archival data in the geography of surveying and mapping industry, which provides important guarantee for the national economy and social development. Efficient storage and retrieval systems for archival data must be built, in order to make effective use of these data. In this paper, OWL language is used to establish the ontology relation model of computer language, and then the ontology reasoning mechanism is studied on this basis. The reasoning model is educed with open source project Jena inference engine. Then the knowledge map of regional geographic ontology is constructed with the powerful ontology graphical representation ability of graph database. Finally, the search system is developed based on the java language, achieving the required retrieval requirements.

Geographic Process Modeling Based on Geographic Ontology

Abstract Considerable attention has been paid to geographic process-based studies in geographic information science research. Finding appropriate methods to express geographic processes is challenging, and working to reveal the dynamic evolution and underlying mechanisms behind these processes is worthwhile. This research proposes a process-centric ontology model that describes the geographical environment from three perspectives, namely, geographic scenes, geographic processes and geographic elements. These three aspects are combined to represent the dynamic changes of geographic phenomena. This research proposes a framework and constructs ten sub-ontologies. These sub-ontologies include the Element ontology, Scene ontology, and Process ontology. A soil erosion process is then selected to demonstrate the applicability of this framework. The entire process is divided into three sub-processes (detachment, transport and deposition), and each sub-process is described by identifying when and where the process occurred, the elements and their reactions, and the changes in the geographic scene. Different relationships among elements, scenes and processes are defined to explain how and why soil erosion occurred. This proposed approach can reveal the underlying mechanisms of geographic scenes, can be used to explore the occurrence and causes of geographic processes and support the complex representation of geographic elements.

Geographic ontology for major disasters: Methodology and implementation

During a catastrophic event, the International Charter11http://www.disasterscharter.org/. "Space and Major Disasters" is regularly activated and provides the rescue teams damage maps prepared by a photo-interpreter team basing on pre and post-disaster satellite images. A satellite image manual processing must be accomplished in most cases to build these maps, a complex and demanding process. Given the importance of time in such critical situations, automatic or semiautomatic tools are highly recommended. Despite the quick treatment presented by automatic processing, it usually presents a semantic gap issue. Our aim is to express expert knowledge using a well-defined knowledge representation method: ontologies and make semantics explicit in geographic and remote sensing applications by taking the ontology advantages in knowledge representation, expression, and knowledge discovery. This research focuses on the design and implementation of a comprehensive geographic ontology in the case of major disasters, that we named GEO-MD, and illustrates its application in the case of Haiti 2010 earthquake. Results show how the ontology integration reduces the semantic gap and improves the automatic classification accuracy.

A novel algorithm for fully automated mapping of geospatial ontologies

Geospatial information is collected from different sources thus making spatial ontologies, built for the same geographic domain, heterogeneous; therefore, different and heterogeneous conceptualizations may coexist. Ontology integrating helps creating a common repository of the geospatial ontology and allows removing the heterogeneities between the existing ontologies. Ontology mapping is a process used in ontologies integrating and consists in finding correspondences between the source ontologies. This paper deals with the “mapping” process of geospatial ontologies which consist in applying an automated algorithm in finding the correspondences between concepts referring to the definitions of matching relationships. The proposed algorithm called “geographic ontologies mapping algorithm” defines three types of mapping: semantic, topological and spatial.

Geography’s “World view”: The ontological issues of geography

Contemporary science philosophy suggests that discussing ontological problems is of fundamental significance broadly within certain specific disciplines. Natural and social science research are inseparable from philosophical guidance; for instance, the philosophy of geography is the ideological basis for geography. The traditional philosophy of geography is methodology-oriented, which primarily emphasizes the “logical structure of geography explanations,” and ignores the discussion of its ontology. This study, in the context of the philosophy of science, explores the relationships between methodology, ontology, and the philosophy of geography, defines the connotations of geographical ontology, analyzes the links and differences between philosophical ontology and scientific ontology of geography, clarifies the nature of geographical ontology, and summaries its theoretical values. The ontology of geography incorporates the philosophically ontological beliefs of geographers and geographical schools and the ontological commitment of the theory of geography. As different geographers hold different philosophical viewpoints, their ontological beliefs are different; one geographical theory asserts an ontological commitment of “what is there,” which determines the nature and types of objectives the theory references. The ontological beliefs of geographers determine their epistemology, methodology, and axiology, and the ontological commitment of a geographical theory is the premise and basis of that theory.

A geo-ontology-based approach to decision-making in emergency management of meteorological disasters

Ontology as a kind of method for knowledge representation is able to provide semantic integration for decision support in emergency management activities of meteorological disasters. We examine a meteorological disaster system as composed of four components: disastrous meteorological events, hazard-inducing environments, hazard-bearing bodies, and emergency management. The geospatial characteristics of these components can be represented with geographical ontology (geo-ontology). In this paper, we propose an ontology representation of domain knowledge of a meteorological disaster system descending from an adapted geospatial foundation ontology, designed to formally conceptualize the domain terms and establish relationships between those concepts. The class hierarchy and relationships of the proposed ontology are implemented finally at top level, domain level/task level, and application level. The potential application of the ontology is illustrated with a case study of prediction of secondary disasters and evacuation decision of a typhoon event. The multi-level ontology model can provide semantic support for before-, during-, after-event emergency management activities such as risk assessment, resource preparedness, and emergency response where the formed concepts and their relationships can be incorporated into reasoning sentences of these decision processes. Furthermore, the ontology model is realized with a universally used intermediate language OWL, which enables it to be used in popular environments. This work will underlie the semantic integration among human beings, between heterogeneous systems and between human beings and systems, enable spatial semantic reasoning, and will be useful in guiding advanced decision support in emergency management of meteorological disasters.

Research and Application of Geographic Ontology Modeling Method Based on OWL

We studied characteristics of geographic ontology which are different from the traditional ontology in detail. Based on detailed analysis of the basic theory, ontology construction method and the basic principles, we discussed the method of the basic attributes of geographic ontology in the expression of geographical objects with complex spatial relationship. We researched on the ontology modeling language based on OWL in the expression of geographic ontology, we introduced the part-whole theory, topology and location theory into OWL modeling primitives to solve geographic object expression between spatial relationships, and we create a geographic ontology based on the existing spatial data which are verified based on the improved OWL.

Objected-oriented remote sensing image classification method based on geographic ontology model

Nowadays, with the development of high resolution remote sensing image and the wide application of laser point cloud data, proceeding objected-oriented remote sensing classification based on the characteristic knowledge of multi-source spatial data has been an important trend on the field of remote sensing image classification, which gradually replaced the traditional method through improving algorithm to optimize image classification results. For this purpose, the paper puts forward a remote sensing image classification method that uses the he characteristic knowledge of multi-source spatial data to build the geographic ontology semantic network model, and carries out the objected-oriented classification experiment to implement urban features classification, the experiment uses protégé software which is developed by Stanford University in the United States, and intelligent image analysis software—eCognition software as the experiment platform, uses hyperspectral image and Lidar data that is obtained through flight in DaFeng City of JiangSu as the main data source, first of all, the experiment uses hyperspectral image to obtain feature knowledge of remote sensing image and related special index, the second, the experiment uses Lidar data to generate nDSM(Normalized DSM, Normalized Digital Surface Model),obtaining elevation information, the last, the experiment bases image feature knowledge, special index and elevation information to build the geographic ontology semantic network model that implement urban features classification, the experiment results show that, this method is significantly higher than the traditional classification algorithm on classification accuracy, especially it performs more evidently on the respect of building classification. The method not only considers the advantage of multi-source spatial data, for example, remote sensing image, Lidar data and so on, but also realizes multi-source spatial data knowledge integration and application of the knowledge to the field of remote sensing image classification, which provides an effective way for objected-oriented remote sensing image classification in the future.

A Framework for Geographic Object-Based Image Analysis (GEOBIA) based on geographic ontology

Abstract. GEOBIA (Geographic Object-Based Image Analysis) is not only a hot topic of current remote sensing and geographical research. It is believed to be a paradigm in remote sensing and GIScience. The lack of a systematic approach designed to conceptualize and formalize the class definitions makes GEOBIA a highly subjective and difficult method to reproduce. This paper aims to put forward a framework for GEOBIA based on geographic ontology theory, which could implement "Geographic entities - Image objects - Geographic objects" true reappearance. It consists of three steps, first, geographical entities are described by geographic ontology, second, semantic network model is built based on OWL(ontology web language), at last, geographical objects are classified with decision rule or other classifiers. A case study of farmland ontology was conducted for describing the framework. The strength of this framework is that it provides interpretation strategies and global framework for GEOBIA with the property of objective, overall, universal, universality, etc., which avoids inconsistencies caused by different experts’ experience and provides an objective model for mage analysis.

World Geographical Ontology Model

days have witnessed an explosive growth in generating data in geographic domain which leads to increasing the applications of geographical information system (GIS). Due to this increasing of GIS, GIS faced the problem of effective management of the spatial information from different resources and in different forms. So, Geographical ontologies have been introduced to the geographical domain not only as a concept model which can represent objects on semantic and knowledge level, but also to integrate geographical data from different sources and in different forms for reasoning and to facilitate knowledge sharing, in addition to assist in recognizing spatial terms. In this research, the World geographical ontology model has been developed to meet the needs of the recognition of the terminologies and the semantic relationships between geographical terms related to any location in the world. It will enable search engine to perform a spatially aware search. The design of World ontology model has been developed based on the analysis and finding relations between different parts of World locations to adopt ontology concepts. Logical formulation has been used to describe the ontology to be able to infer more intelligent relations. Then the model has been implemented using protege tool, a sample of related data has been collected and finally the system has been tested. Query answers integrate data from different sources. KeywordsGeo-Ontology, OWL, Semantic Web

Geographic Ontologies, Gazetteers and Multilingualism

Different languages imply different visions of space, so that terminologies are different in geographic ontologies. In addition to their geometric shapes, geographic features have names, sometimes different in diverse languages. In addition, the role of gazetteers, as dictionaries of place names (toponyms), is to maintain relations between place names and location. The scope of geographic information retrieval is to search for geographic information not against a database, but against the whole Internet: but the Internet stores information in different languages, and it is of paramount importance not to remain stuck to a unique language. In this paper, our first step is to clarify the links between geographic objects as computer representations of geographic features, ontologies and gazetteers designed in various languages. Then, we propose some inference rules for matching not only types, but also relations in geographic ontologies with the assistance of gazetteers.

Building a Geographical Ontology for the Nile River

Building a Geographical Ontology for the Nile River

A model of a digital biological ecosystem

In this paper, we propose a model of a digital biological ecosystem of fish. Collections of fish data by scientists in Malaysia have been converted and developed into a digital biological ecosystem using computational technology and methods. We used a combination of Geographical Information System, databases and ontologies for storing, retrieving and visualizing fish species distribution and their abundance as well as their relationship with the environment. We used ArcGIS to build the fish and environmental geodatabase, which is linked to Google Earth for real-time visualization, and Surfer 8 to create contour maps which are superimposed as layers on the ArcGIS interface. Individual fish species on the map are hyperlinked to the FishBase Portal (http://www.fishbase.org/) while the published map is displayed using ArcReader. The three databases that we created contain information about fish species, environment and contours. These are also integrated using a fish ontology which will link all the available information for various fish species. The proposed system can be adopted by marine scientists to better convince economists, fishery managers, coastal developers and government bodies who make important decisions on conservation policies and laws.

A Method for Building Geographical Conditions Ontology Attribute Library: Taking Tianjin as an Example

As a new tool and new approach in the scientific development of surveying and mapping service, the mission of geographic national condition monitoring is to provide objective, fair and accurate thematic information for integrated decision-making at all levels of government. How to realize the semantic sharing of geographic national condition thematic information is the key problem for geographic national condition to provide cross-industry, cross-sector and interdisciplinary information service. Based on ontology theory, this paper proposed a method for building geographic national conditions ontology attribute database. Tianjin geographic national conditions monitoring content and its service users are selected as knowledge domain and conceptualization and formal representation is made for geographic national information, protégé software is used to build ontology attribute library.