Using Geographic Ontologies and Geo-Characterization to Represent Geographic Scenarios

Huang, Yi; Yuan, May; Sheng, Yehua; Min, Xiangqiang; Cao, Yuwei

doi:10.3390/ijgi8120566

Cited by 15 publications

(9 citation statements)

References 43 publications

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“…Thus, the geographic knowledge graph has become a research hotspot in the past few years [37,38]. These knowledge graphs aim to represent human cognition of the geographic world, generating a knowledge system that expresses the interaction between geographic concepts, entities, and elements [39]. To address the spatiotemporal characteristics of geographic problems, Zheng et al proposed a generalized framework for geospatial knowledge graph construction.…”

Section: Related Workmentioning

confidence: 99%

The Construction of Urban Rainstorm Disaster Event Knowledge Graph Considering Evolutionary Processes

Zou,

Huang,

Wang

et al. 2024

Water

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Rainstorm disasters pose a significant threat to the sustainable development of urban areas, and effectively organizing diverse information sources about them is crucial for emergency management. In light of recent advances in knowledge graph theory and application technology, their notable knowledge integration and representation capabilities may offer support for dynamic monitoring and decision-making processes concerning urban rainstorm disaster events. However, conventional models do not adequately capture the spatiotemporal characteristics of these events. To fill this gap, we analyze the essence of urban rainstorm disaster events and divide their evolution into four stages, namely, pregnant, development, continuous, and decline stages. On this basis, a multilevel knowledge representation model is proposed from four layers, which are event, object–state, feature, and relationship layers, by analyzing the components and dynamic characteristics from the mechanism of urban rainstorm disaster events. The proposed model can not only express the comprehensive structure and relationships within urban rainstorm events, but also emphasize the evolution of disaster events through a series of ordered states. Moreover, we test the utility of the constructed knowledge graph through a case study of the Zhengzhou 720 rainstorm. The case study first validates that the selected machine learning models can extract the urban rainstorm disaster event information accurately by comparing them with some mainstream models. Then, it demonstrates that the knowledge graph is practical in the field of disaster knowledge representation, and disaster condition retrieval. Additionally, since the knowledge graph can show the evolution of a disaster event throughout its full life cycle, it can promote the understanding of the mechanisms of urban rainstorm disasters and pave the way for future applications of disaster prevention and reduction.

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Section: Related Workmentioning

confidence: 99%

The Construction of Urban Rainstorm Disaster Event Knowledge Graph Considering Evolutionary Processes

Zou,

Huang,

Wang

et al. 2024

Water

Self Cite

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“…Therefore, it is important to characterize their spatiotemporal information. Contemporary knowledge graphs represent spatiotemporal knowledge as the attribute of elements and neglect the evolution and interaction mechanism (Huang et al, 2019). Based on this, we improve the conventional 'state-process' model (Figure 4), and apply it in the representation of geoscience knowledge (Ye et al, 2020(Ye et al, , 2021.…”

Section: Geoscience Knowledge Representation Modelmentioning

confidence: 99%

Geoscience Knowledge Graph (GeoKG): Development, construction and challenges

Zhang

Huang

Zhang

et al. 2022

Transactions in GIS

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Big earth data is a cross‐domain of geoscience and information science, which provides a novel perspective for solving geoscience problems. Most contemporary research is driven by data but neglect the potential value of knowledge. As a new scientific language in Geoscience, GeoKG is essential for understanding, representing, and mining geoscience knowledge, and can contribute to the integration of big earth data, geoscience knowledge, and geoscience models. However, research on GeoKG lack spatiotemporal perspectives in knowledge cognition, representation, acquisition and management. To this end, this article first outlines a cognitive mechanism from the human–machine double perspective and categorizes the characteristics and content of geoscience knowledge. To express evolution and complex natural rules, a knowledge representation framework is proposed through ‘state‐process’ and ‘condition‐result’ models. Aiming at multimodal data, a workflow is put forward to acquire knowledge from a small sample, a knowledge graph, a map, and a schematic diagram. Furthermore, we discuss the organization of GeoKG by improving existing data models, developing spatiotemporal correlation indexing and advancing knowledge graph completion. The concrete construction process of GeoKG is analyzed thoroughly in this study, which can support the synthetic analysis of big earth data, promote the development of knowledge engineering and provide a foundation for improving intelligent geoscience.

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“…The work in [176] illustrates Geographic Scenarios [177], a notion developed on the basis of General System Theory [178] integrating spatial, process, and relational information related to geographical elements and georeferenced events. In contrast with reductionist approaches (such as those dividing geo-entities into themes), Geographic Scenarios propose a holistic view that should be better suited to represent hierarchical connections among geo-entities.…”

Section: Holistic Map Representation With Geographic Scenariosmentioning

confidence: 99%

Implicit, Formal, and Powerful Semantics in Geoinformation

Bordogna

Fugazza

d’Aragona

et al. 2021

IJGI

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Distinct, alternative forms of geosemantics, whose classification is often ill-defined, emerge in the management of geospatial information. This paper proposes a workflow to identify patterns in the different practices and methods dealing with geoinformation. From a meta-review of the state of the art in geosemantics, this paper first pinpoints “keywords” representing key concepts, challenges, methods, and technologies. Then, we illustrate several case studies, following the categorization into implicit, formal, and powerful (i.e., soft) semantics depending on the kind of their input. Finally, we associate the case studies with the previously identified keywords and compute their similarities in order to ascertain if distinguishing methodologies, techniques, and challenges can be related to the three distinct forms of semantics. The outcomes of the analysis sheds some light on the diverse methods and technologies that are more suited to model and deal with specific forms of geosemantics.

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Using Geographic Ontologies and Geo-Characterization to Represent Geographic Scenarios

Cited by 15 publications

References 43 publications

The Construction of Urban Rainstorm Disaster Event Knowledge Graph Considering Evolutionary Processes

The Construction of Urban Rainstorm Disaster Event Knowledge Graph Considering Evolutionary Processes

Geoscience Knowledge Graph (GeoKG): Development, construction and challenges

Implicit, Formal, and Powerful Semantics in Geoinformation

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