Semantic 3D City Database — An enabler for a dynamic geospatial knowledge graph

Chadzynski, Arkadiusz; Krdžavac, Nenad; Farazi, Feroz; Lim, Mei Qi; Li, Shiying; Grisiute, Ayda; Herthogs, Pieter; Richthofen, Aurel von; Cairns, Stephen; Kraft, Markus

doi:10.1016/j.egyai.2021.100106

Cited by 38 publications

(31 citation statements)

References 21 publications

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“…The World Avatar project aims to create a dynamic world model that is generic and all-encompassing, with a focus so far geared toward the decarbonization of energy and the process industries (Pan et al, 2015(Pan et al, , 2016Kleinelanghorst et al, 2017;Zhang et al, 2017;Devanand et al, 2020Devanand et al, , 2021Atherton et al, 2021), Data-Centric Engineering e3-3 city planning (Chadzynski et al, 2021;von Richthofen et al, 2021), and chemistry (Krdzavac et al, 2019;Farazi et al, 2020a;Mosbach et al, 2020;Zhou et al, 2021). The world model is implemented as a dynamic knowledge graph that is built using Semantic Web technologies.…”

Section: The World Avatarmentioning

confidence: 99%

“…GeoSPARQL extends the SPARQL query language to support the querying of and reasoning about geospatial information. However, the GeoSPARQL support offered by different triple stores remains limited and inconsistent (Chadzynski et al, 2021;Jovanovik et al, 2021), for example RDF4J (Eclipse Foundation, 2021a) offers "partial GeoSPARQL support" (Eclipse Foundation, 2021b) whilst Blazegraph (2021), which is used in this work, does not support GeoSPARQL, but offers simple geospatial reasoning via a custom interface.…”

Section: Geospatial Datamentioning

confidence: 99%

“…Figure 4 shows the TBox of OntoCropMapGML. It incorporates properties and ranges described by a number of general-purpose ontologies, including data and object properties defined by OntoCityGML (Chadzynski et al, 2021), which defines terms used to describe built environments that can appropriately describe certain geospatial elements of the CROME data. The classes and relationships that have been defined elsewhere are prefixed with the namespace of their native ontologies.…”

Section: Ontocropmapgmlmentioning

confidence: 99%

“…The name POLYGON-2-14 was an arbitrary choice, but was chosen to indicate that the data type represents a 2D object described by 14 data values (seven pairs of latitude and longitude coordinates to represent a hexagon, where the first and last pair of coordinates are the same for a closed shape). This method of defining and naming custom data types is extensible and mirrors the approach developed by Chadzynski et al (2021), where a family of custom data types were used for the purpose of describing 3D city data. An archived version of the custom vocabulary and data type is provided as part of the Research Data associated with this article.…”

Section: Knowledge Graph Deploymentmentioning

confidence: 99%

“…The digital twin currently includes a description of all the power plants in the UK (Atherton et al, 2021). Work to establish data pipelines to describe buildings (Chadzynski et al, 2021), the gas transmission network, geospatial climate data and the potential for solar and wind power in the UK is underway. The representation of biomass availability and land use within this digital twin will enrich the description of the UK provided by the base world.…”

Section: Introductionmentioning

confidence: 99%

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Universal Digital Twin: Land use

Akroyd

Harper

Soutar

et al. 2022

DCE

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This article develops an ontological description of land use and applies it to incorporate geospatial information describing land coverage into a knowledge-graph-based Universal Digital Twin. Sources of data relating to land use in the UK have been surveyed. The Crop Map of England (CROME) is produced annually by the UK Government and was identified as a valuable source of open data. Formal ontologies to represent land use and the geospatial data arising from such surveys have been developed. The ontologies have been deployed using a high-performance graph database. A customized vocabulary was developed to extend the geospatial capabilities of the graph database to support the CROME data. The integration of the CROME data into the Universal Digital Twin is demonstrated in two use cases that show the potential of the Universal Digital Twin to share data across sectors. The first use case combines data about land use with a geospatial analysis of scenarios for energy provision. The second illustrates how the Universal Digital Twin could use the land use data to support the cross-domain analysis of flood risk. Opportunities for the extension and enrichment of the ontologies, and further development of the Universal Digital Twin are discussed.

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