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dc.contributor.authorChadzynski, A
dc.contributor.authorLi, S
dc.contributor.authorGrisiute, A
dc.contributor.authorFarazi, F
dc.contributor.authorLindberg, C
dc.contributor.authorMosbach, S
dc.contributor.authorHerthogs, P
dc.contributor.authorKraft, M
dc.date.accessioned2022-02-26T00:30:30Z
dc.date.available2022-02-26T00:30:30Z
dc.date.issued2022-05
dc.identifier.issn2666-5468
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334464
dc.description.abstractThis paper presents a system of autonomous intelligent software agents, based on a cognitive architecture, capable of automated instantiation, visualisation and analysis of multifaceted City Information Models in dynamic geospatial knowledge graphs. Design of JPS Agent Framework and Routed Knowledge Graph Access components was required in order to provide backbone infrastructure for an intelligent agent system as well as technology agnostic knowledge graph access enabling automation of multi-domain data interoperability. Development of CityImportAgent, CityExportAgent and DistanceAgent showcased intelligent automation capabilities of the Cities Knowledge Graph. The agents successfully created a semantic model of Berlin in LOD 2, compliant with CityGML 2.0 standard and consisting of 419 909 661 triples described using OntoCityGML. The system of agents also visualised and analysed the model by autonomously tracking interactions with a web interface as well as enriched the model by adding new information to the knowledge graph. This way it was possible to design a geospatial information system able to meet demands imposed by the Industry 4.0 and link it with the other multi-domain knowledge representations of The World Avatar.
dc.publisherElsevier BV
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleSemantic 3D City Agents—An intelligent automation for dynamic geospatial knowledge graphs
dc.typeArticle
dc.publisher.departmentDepartment of Chemical Engineering And Biotechnology
dc.date.updated2022-02-25T09:38:56Z
prism.endingPage100137
prism.number100137
prism.publicationDate2022
prism.publicationNameEnergy and AI
prism.startingPage100137
prism.volume8
dc.identifier.doi10.17863/CAM.81881
dcterms.dateAccepted2022-01-13
rioxxterms.versionofrecord10.1016/j.egyai.2022.100137
rioxxterms.versionVoR
dc.contributor.orcidKraft, M [0000-0002-4293-8924]
dc.identifier.eissn2666-5468
rioxxterms.typeJournal Article/Review
pubs.funder-project-idNational Research Foundation Singapore (via Cambridge Centre for Advanced Research and Education in Singapore (CARES)) (unknown)
cam.issuedOnline2022-02-05
cam.depositDate2022-02-25
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International