Station–city integration cyberspace behaves as an interdisciplinary field of intelligent transportation and smart city, also a representative scenario in the Architecture, Engineering and Construction (AEC) sector. Due to the growing demands in data integration research, the intelligent operation and maintenance (O&M) of the station–city integration cyberspace needs to implement semantic ontology, which is suitable for semantic web construction. To achieve semantic information fusion of multi-source heterogeneous data, and clarify the decision-making role of various types of data on specific operational goals, this article proposed a framework for semantic ontology model construction, based on the deployed sensor network. Specifically, an ontology model for station–city integration cyberspace O&M was constructed, incorporating sensor data mainly from five categories, named structure, environment, crowd flow, emergency events, and energy consumption, respectively. Subsequently, the library in Python was utilized to assign data flow to static semantic models. Furthermore, a semantic web inference engine was generated using decision rules, ultimately completing risk early warning and equipment maintenance for the sensor network. Finally, a case study was conducted for the station–city integration O&M scenario in Shenzhen North Station, and the experimental results demonstrated the applicability and effectiveness, providing robust, intelligent data support for intelligent O&M.
AncioneG.AnsaldiS. M.BragattoP.AgnelloP.MilazzoM. F. (2024). An ontological approach increasing the knowledge about equipment ageing including the effects of current transitions. Journal of Loss Prevention in the Process Industries, 92, 105453. https://doi.org/10.1016/j.jlp.2024.105453
2.
AngsuchotmeteeC.ChbeirR.CardinaleY. (2020). MSSN-onto: An ontology-based approach for flexible event processing in multimedia sensor networks. Future Generation Computer Systems, 114, 1140–1158. https://doi.org/10.1016/j.future.2018.01.044
3.
CanitoA.CorchadoJ.MarreirosG. (2022). A systematic review on time-constrained ontology evolution in predictive maintenance. Artificial Intelligence Review, 55(4), 3183–3211. https://doi.org/10.1007/s10462-021-10079-z
4.
CardinaleY.FreitesG.ValderramaE.AguileraA.AngsuchotmeteeC. (2022). Semantic framework of event detection in emergency situations for smart buildings. Digital Communications and Networks, 8(1), 64–79. https://doi.org/10.1016/j.dcan.2021.06.005
5.
ChandraR.AgarwalS.SinghN. (2022). Semantic sensor network ontology-based decision support system for forest fire management. Ecological Informatics, 72, 101821. https://doi.org/10.1016/j.ecoinf.2022.101821
6.
ChenG.JiangT.WangM.TangX.JiW. (2020). Modeling and reasoning of iot architecture in semantic ontology dimension. Computer Communications, 153, 580–594. https://doi.org/10.1016/j.comcom.2020.02.006
7.
ChenJ.YanZ.FanR. (2023). Elements of the core area transport system oriented to station–city integration. Computers and Electrical Engineering, 110, 108842. https://doi.org/10.1016/j.compeleceng.2023.108842
8.
CivitareseG.SztylerT.RiboniD.BettiniC.StuckenschmidtH. (2021). Polaris: Probabilistic and ontological activity recognition in smart-homes. IEEE Transactions on Knowledge and Data Engineering, 33(1), 209–223. https://doi.org/10.1109/TKDE.2019.2930050
9.
CuiC.XuM.XuC.ZhangP.ZhaoJ. (2023). An ontology-based probabilistic framework for comprehensive seismic risk evaluation of subway stations by combining Monte Carlo simulation. Tunnelling and Underground Space Technology, 135, 105055. https://doi.org/10.1016/j.tust.2023.105055
10.
De NicolaA.VillaniM. L. (2021). Smart city ontologies and their applications: A systematic literature review. Sustainability, 13(10), 5778. https://doi.org/10.3390/su13105578
11.
DiamantiniC.MircoliA.PotenaD.StortiE. (2023). Process-aware iiot knowledge graph: A semantic model for industrial IoT integration and analytics. Future Generation Computer Systems, 139, 224–238. https://doi.org/10.1016/j.future.2022.10.003
12.
DiaoM. (2019). Towards sustainable urban transport in Singapore: Policy instruments and mobility trends. Transport Policy, 81, 320–330. https://doi.org/10.1016/j.tranpol.2018.05.005
13.
DinhD. H.DoP.HoangV. T.VoN. T.BangT. Q. (2024). A predictive maintenance policy for manufacturing systems considering degradation of health monitoring device. Reliability Engineering & System Safety, 248, 110177. https://doi.org/10.1016/j.ress.2024.110177
14.
DuobieneS.RatautasK.TrusovasR.RagulisP.ŠlekasG.SimniškisR.RaciukaitisG. (2022). Development of wireless sensor network for environment monitoring and its implementation using sail technology. Sensors, 22(14), 5343. https://doi.org/10.3390/s22145343
15.
GianniosG.MpaltadorosL.AlepopoulosV.GrammatikopoulouM.StavropoulosT. G.NikolopoulosS.LazarouI.TsolakiM.KompatsiarisI. (2024). A semantic framework to detect problems in activities of daily living monitored through smart home sensors. Sensors, 24(4), 1107. https://doi.org/10.3390/s24041107
16.
GlimmB.HorrocksI.MotikB.StoilosG.WangZ. (2014). Hermit: An owl 2 reasoner. Journal of Automated Reasoning, 53, 245–269.
17.
Gouda MohamedA.GhalyJ. E.MarzoukM. (2024). Revolutionizing semantic integration of maintenance cost prediction for building systems using artificial neural networks. Journal of Building Engineering, 96, 110416. https://doi.org/10.1016/j.jobe.2024.110416
18.
GruberT. R. (1995). Toward principles for the design of ontologies used for knowledge sharing?International Journal of Human-Computer Studies, 43(5-6), 907–928. https://doi.org/10.1006/ijhc.1995.1081
19.
GuoS.NiuG.WangZ.PunM. O.YangK. (2021). An indoor knowledge graph framework for efficient pedestrian localization. IEEE Sensors Journal, 21(4), 5151–5163. https://doi.org/10.1109/JSEN.2020.3029098
20.
GuyoE. D.HartmannT.SnydersS. (2023). An ontology to represent firefighters data requirements during building fire emergencies. Advanced Engineering Informatics, 56, 101992. https://doi.org/10.1016/j.aei.2023.101992
21.
HallerA.JanowiczK.CoxS.LefrançoisM.TaylorK.PhuocD.LiebermanJ.García CastroR.AtkinsonR.StadlerC. (2018). The modular SSN ontology: A joint W3C and OGC standard specifying the semantics of sensors, observations, sampling, and actuation. Semantic Web, 10(1), 9–32. https://doi.org/10.3233/sw-180320
22.
HuangC. Y.ChiangY. H.TsaiF. (2022). An ontology integrating the open standards of city models and internet of things for smart-city applications. IEEE Internet of Things Journal, 9(20), 20444–20457. https://doi.org/10.1109/JIOT.2022.3178903
23.
JanowiczK.HallerA.CoxS. J. D.Le PhuocD.LefrancoisM. (2019). Sosa: A lightweight ontology for sensors, observations, samples, and actuators. Journal of Web Semantics, 56, 1–10. https://doi.org/10.1016/j.websem.2018.06.003
24.
KeL.FuruyaK.LuoS. (2021). Case comparison of typical transit-oriented-development stations in Tokyo district in the context of sustainability: Spatial visualization analysis based on fahp and gis. Sustainable Cities and Society, 68, 102788. https://doi.org/10.1016/j.scs.2021.102788
25.
KeetC. M.KhanZ. C. (2024). Discerning and characterising types of competency questions for ontologies. https://arxiv.org/abs/2412.13688.
26.
KurtevaA.ChhetriT. R.TauqeerA.HilscherR.FenselA.NagornyK.CorreiaA.ZilverbergA.SchestakovS.FunkeT.DemidovaE. (2023). The smashhitcore ontology for gdpr-compliant sensor data sharing in smart cities. Sensors, 23(13), 6188. https://doi.org/10.3390/s23136188
27.
KusterC.HippolyteJ. L.RezguiY. (2020). The UDSA ontology: An ontology to support real-time urban sustainability assessment. Advances in Engineering Software, 140, 102731. https://doi.org/10.1016/j.advengsoft.2019.102731
28.
LiK. (2022). Application of artificial intelligence system based on wireless sensor network in enterprise management. Computational Intelligence and Neuroscience, 2022, 2169521. https://doi.org/10.1155/2022/2169521
29.
LiR.MoT.YangJ.JiangS.LiT.LiuY. (2021). Ontologies-based domain knowledge modeling and heterogeneous sensor data integration for bridge health monitoring systems. IEEE Transactions on Industrial Informatics, 17(1), 321–332. https://doi.org/10.1109/TII.2020.2967561
30.
LiX.YangD.YuanJ.DonkersA.LiuX. (2022). Bim-enabled semantic web for automated safety checks in subway construction. Automation in Construction, 141, 104454. https://doi.org/10.1016/j.autcon.2022.104454
31.
MaZ. M.LiD.LuJ.MaR.YanL. (2023). Rdf(s) store in object-relational databases. Journal of Database Management, 35(1), 1–32.
32.
Montero JimenezJ. J.SchwartzS.VingerhoedsR.GrabotB.SalaünM. (2020). Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics. Journal of Manufacturing Systems, 56, 539–557. https://doi.org/10.1016/j.jmsy.2020.07.008
33.
NavabianN.BeskhyrounS.MatulichJ. (2020). Development of wireless smart sensor network for vibration-based structural health monitoring of civil structures. Structure and Infrastructure Engineering, 18(3), 345–361. https://doi.org/10.1080/15732479.2020.1850801
Periñán PascualC. (2023). From smart city to smart society: A quality-of-life ontological model for problem detection from user-generated content. Applied Ontology, 18(3), 263–306. https://doi.org/10.3233/AO-230281
36.
SantosR.EgglyG.GutierrezJ.ChesñevarC. I. (2023). Extending the IoT-stream model with a taxonomy for sensors in sustainable smart cities. Sustainability, 15(8), 6594. https://doi.org/10.3390/su15086594
37.
SayahZ.KazarO.LejdelB.LaouidA.GhenabziaA. (2021). An intelligent system for energy management in smart cities based on big data and ontology. Smart and Sustainable Built Environment, 10(2), 169–192. https://doi.org/10.1108/SASBE-07-2019-0087
38.
ShiJ.PanZ.JiangL.ZhaiX. (2023). An ontology-based methodology to establish city information model of digital twin city by merging bim, gis, and iot. Advanced Engineering Informatics, 57, 102114. https://doi.org/10.1016/j.aei.2023.102114
39.
SobralT.GalvãoT.BorgesJ. (2021). Knowledge-assisted visualization of multi-level origin-destination flows using ontologies. IEEE Transactions on Intelligent Transportation Systems, 22(4), 2168–2177. https://doi.org/10.1109/TITS.2021.3056228
40.
StavropoulosT. G.MeditskosG.LazarouI.MpaltadorosL.PapagiannopoulosS.TsolakiM.KompatsiarisI. (2021). Detection of health-related events and behaviours from wearable sensor lifestyle data using symbolic intelligence: A proof-of-concept application in the care of multiple sclerosis. Sensors, 21(18), 6230. https://doi.org/10.3390/s21186230
41.
TanJ.QiuQ.GuoW.LiT. (2021). Research on the construction of a knowledge graph and knowledge reasoning model in the field of urban traffic. Sustainability, 13, 3191. https://doi.org/10.3390/su13063191
42.
TsoutsaP.FitsilisP.IatrellisO. (2022). Towards an ontology for smart city competences. In Proceedings of the 25th Pan-Hellenic conference on informatics, PCI ’21, (pp. 254–259). New York, NY, USA: association for computing machinery. ISBN 9781450395557. https://doi.org/10.1145/3503823.3503871.
43.
van DinterR.TekinerdoganB.CatalC. (2022). Predictive maintenance using digital twins: A systematic literature review. Information and Software Technology, 151, 107008. https://doi.org/10.1016/j.infsof.2022.107008
44.
ViktorovicM.YangD.VriesBd (2020). Connected traffic data ontology (CTDO) for intelligent urban traffic systems focused on connected (semi) autonomous vehicles. Sensors, 20(10), 2961. https://doi.org/10.3390/s20102961
45.
VölkerJ.FleischhackerD.StuckenschmidtH. (2015). Automatic acquisition of class disjointness. Journal of Web Semantics, 35, 124–139. https://doi.org/10.1016/j.websem.2015.07.001
46.
WangX.ZhangN.ZhangY.ShiZ. (2017). Forecasting of short-term metro ridership with support vector machine online model. Journal of Advanced Transportation, 2018(1), 3189238. https://doi.org/10.1155/2018/3189238
47.
XiJ.WuJ.WuM. (2023). Design and construction of lightweight domain ontology of tectonic geomorphology. Journal of Earth Sciences, 34(6), 1350–1357. https://doi.org/10.1007/s12583-022-1779-x
48.
XuZ.HuoH.PangS. (2022). Identification of environmental pollutants in construction site monitoring using association rule mining and ontology-based reasoning. Buildings, 12(12), 2111. https://doi.org/10.3390/buildings12122111
49.
YangC.ZhengY.TuX.Ala-LaurinahoR.AutiosaloJ.SeppänenO.TammiK. (2023). Ontology-based knowledge representation of industrial production workflow. Advanced Engineering Informatics, 58, 102185. https://doi.org/10.1016/j.aei.2023.102185
