Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications

Li, Wenwen; Wang, Sizhe; Wu, Sheng; Gu, Zhining; Tian, Yuanyuan

doi:10.1016/j.compenvurbsys.2022.101884

Cited by 17 publications

(3 citation statements)

References 18 publications

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“…However, finite state machines can only represent a finite number of states. A knowledge graph is essentially a large-scale semantic network that can represent richer semantic relationships and contain more entities than a standard semantic network [29]. Several studies on different areas of hazards have used knowledge graphs to formally represent hazard information.…”

Section: Formal Methods For Hazard Event Informationmentioning

confidence: 99%

Knowledge Graph Representation of Multi-Source Urban Storm Surge Hazard Information Based on Spatio-Temporal Coding and the Hazard Events Ontology Model

Lei,

Wang,

Han

et al. 2024

IJGI

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Coastal cities are increasingly vulnerable to urban storm surge hazards and the secondary hazards they cause (e.g., coastal flooding). Accurate representation of the spatio-temporal process of hazard event development is essential for effective emergency response. However, current knowledge graph representations face the challenge of integrating multi-source information with various spatial and temporal scales. To address this challenge, we propose a new information model for storm surge hazard events, involving a two-step process. First, a hazard event ontology is designed to model the components and hierarchical relationships of hazard event information. Second, we utilize multi-scale time segment integer coding and geographical coordinate subdividing grid coding to create a spatio-temporal framework, for modeling spatio-temporal features and spatio-temporal relationships. Using the 2018 typhoon Mangkhut storm surge event in Shenzhen as a case study and the hazard event information model as a schema layer, a storm surge event knowledge graph is constructed, demonstrating the integration and formal representation of heterogeneous hazard event information and enabling the fast retrieval of disasters in a given spatial or temporal range.

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Section: Formal Methods For Hazard Event Informationmentioning

confidence: 99%

Knowledge Graph Representation of Multi-Source Urban Storm Surge Hazard Information Based on Spatio-Temporal Coding and the Hazard Events Ontology Model

Lei,

Wang,

Han

et al. 2024

IJGI

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“…However, it is quite likely that this technique could be used in the future to link city models and external registers/databases/operational systems, but this would require that the external data are stored as (or, at least, are easy to convert to) linked data (RDF data). Furthermore, substantial work on developing ontologies (for work on CityGML ontologies see, for example, [84]), aligning ontologies, and improving RDF stores to handle geospatial data (see, for example, [85,86]) is still required. The important aspect for the current design decision is that 3CIM uses a data model (where objects are linked using unique ID-numbers) that, later on, may be transformed to a linked data solution.…”

Section: Linking 3cim To External Data-using Linked Datamentioning

confidence: 99%

Future Swedish 3D City Models—Specifications, Test Data, and Evaluation

Uggla¹,

Olsson

Abdi

et al. 2023

IJGI

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Three-dimensional city models are increasingly being used for analyses and simulations. To enable such applications, it is necessary to standardise semantically richer city models and, in some cases, to connect the models with external data sources. In this study, we describe the development of a new Swedish specification for 3D city models, denoted as 3CIM, which is a joint effort between the three largest cities in Sweden—Stockholm, Gothenburg, and Malmö. Technically, 3CIM is an extension of the OGC standard CityGML 2.0, implemented as an application domain extension (ADE). The ADE is semantically thin, mainly extending CityGML 2.0 to harmonise with national standards; in contrast, 3CIM is mainly based on linkages to external databases, registers, and operational systems for the semantic part. The current version, 3CIM 1.0, includes various themes, including Bridge, Building, Utility, City Furniture, Transportation, Tunnel, Vegetation, and Water. Three test areas were created with 3CIM data, one in each city. These data were evaluated in several use-cases, including visualisation as well as daylight, noise, and flooding simulations. The conclusion from these use-cases is that the 3CIM data, together with the linked external data sources, allow for the inclusion of the necessary information for the visualisation and simulations, but extract, transform, and load (ETL) processes are required to tailor the input data. The next step is to implement 3CIM within the three cities, which will entail several challenges, as discussed at the end of the paper.

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“…As the domain data in various knowledge graphs proliferate in the research community, challenges also arise for non‐developers as well as knowledge engineers to access and understand massive graph‐ready datasets (Li, 2022; Li et al, 2022; Wang & Li, 2019). In particular, it has become increasingly challenging to enable open access to the appropriate graph data in the appropriate form for the appropriate end users.…”

Section: Introductionmentioning

confidence: 99%

GeoGraphViz: Geographically constrained 3D force‐directed graph for knowledge graph visualization

Wang

2023

Transactions in GIS

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Knowledge graphs are a key technique for linking and integrating cross-domain data, concepts, tools, and knowledge to enable data-driven analytics. As much of the world's data have become massive in size, visualizing graph entities and their interrelationships intuitively and interactively has become a crucial task for ingesting and better utilizing graph content to support semantic reasoning, discovering hidden knowledge discovering, and better scientific understanding of geophysical and social phenomena. Despite the fact that many such phenomena (e.g., disasters) have clear spatial footprints and geographic properties, their location information is considered only as a textual label in existing graph visualization tools, limiting their capability to reveal the geospatial distribution patterns of the graph nodes. In addition, most graph visualization techniques rely on 2D graph visualization, which constrains the dimensions of information that can be presented and lacks support for graph structure examination from multiple angles. To tackle the above challenges, we developed a novel 3D map-based graph visualization algorithm to enable interactive exploration of graph content and patterns in a spatially explicit manner.The algorithm extends a 3D force directed graph by integrating a web map, an additional geolocational force, and a force balancing variable that allows for the dynamic adjustment of the 3D graph structure and layout. This mechanism helps create a balanced graph view between the semantic forces among the graph nodes and the attractive force from

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Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications

Cited by 17 publications

References 18 publications

Knowledge Graph Representation of Multi-Source Urban Storm Surge Hazard Information Based on Spatio-Temporal Coding and the Hazard Events Ontology Model

Knowledge Graph Representation of Multi-Source Urban Storm Surge Hazard Information Based on Spatio-Temporal Coding and the Hazard Events Ontology Model

Future Swedish 3D City Models—Specifications, Test Data, and Evaluation

GeoGraphViz: Geographically constrained 3D force‐directed graph for knowledge graph visualization

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