Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164033
Title: Semantic 3D city agents—an intelligent automation for dynamic geospatial knowledge graphs
Authors: Chadzynski, Arkadiusz
Li, Shiying
Grisiute, Ayda
Farazi, Feroz
Lindberg, Casper
Mosbach, Sebastian
Herthogs, Pieter
Kraft, Markus
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Chadzynski, A., Li, S., Grisiute, A., Farazi, F., Lindberg, C., Mosbach, S., Herthogs, P. & Kraft, M. (2022). Semantic 3D city agents—an intelligent automation for dynamic geospatial knowledge graphs. Energy and AI, 8, 100137-. https://dx.doi.org/10.1016/j.egyai.2022.100137
Journal: Energy and AI
Abstract: This 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.
URI: https://hdl.handle.net/10356/164033
ISSN: 2666-5468
DOI: 10.1016/j.egyai.2022.100137
Schools: School of Chemical and Biomedical Engineering 
Organisations: Cambridge Centre for Advanced Research and Education in Singapore (CARES)
Rights: © 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Journal Articles

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