In this paper, we model a knowledge graph based on graph neural networks, conduct an in-depth study on building knowledge graph embeddings for policing cases, and design a graph neural network-enhanced knowledge graph framework. In detail, we use the label propagation algorithm (LPA) to assist the convolutional graph network (GCN) in training the edge weights of the knowledge graph to construct a policing case prediction method. This improves the traditional convolutional neural network from a single-channel network to a multichannel network to accommodate the multiple feature factors of policing cases. In addition, this expands the perceptual field of the convolutional neural network to improve prediction accuracy. The experimental results show that the multichannel convolutional neural network's prediction accuracy can reach 87.7%. To ensure the efficiency of the security case analysis network, an efficient pairwise feature extraction base module is added to enhance the backbone network, which reduces the number of parameters of the whole network and decreases the complexity of operations. We experimentally demonstrate that this method achieves a better balance of efficiency and performance by obtaining approximate results with 53.5% fewer floating-point operations and 70.2% fewer number parameters than its contemporary work.
Citation: Hongqiang Zhu. A graph neural network-enhanced knowledge graph framework for intelligent analysis of policing cases[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 11585-11604. doi: 10.3934/mbe.2023514
In this paper, we model a knowledge graph based on graph neural networks, conduct an in-depth study on building knowledge graph embeddings for policing cases, and design a graph neural network-enhanced knowledge graph framework. In detail, we use the label propagation algorithm (LPA) to assist the convolutional graph network (GCN) in training the edge weights of the knowledge graph to construct a policing case prediction method. This improves the traditional convolutional neural network from a single-channel network to a multichannel network to accommodate the multiple feature factors of policing cases. In addition, this expands the perceptual field of the convolutional neural network to improve prediction accuracy. The experimental results show that the multichannel convolutional neural network's prediction accuracy can reach 87.7%. To ensure the efficiency of the security case analysis network, an efficient pairwise feature extraction base module is added to enhance the backbone network, which reduces the number of parameters of the whole network and decreases the complexity of operations. We experimentally demonstrate that this method achieves a better balance of efficiency and performance by obtaining approximate results with 53.5% fewer floating-point operations and 70.2% fewer number parameters than its contemporary work.
[1] |
Y. Li, H. Ma, L. Wang, S. Mao, G. Wang, Optimized content caching and user association for edge computing in densely deployed heterogeneous networks, IEEE Trans. Mob. Comput., 21 (2022), 2130−2142. https://doi.org/10.1109/TMC.2020.3033563 doi: 10.1109/TMC.2020.3033563
![]() |
[2] |
Z. Guo, K. Yu, A. K. Bashir, D. Zhang, Y. D. Al-Otaibi, M. Guizani, Deep information fusion-driven POI scheduling for mobile social networks, IEEE Network, 36 (2022), 210−216. https://doi.org/10.1109/MNET.102.2100394 doi: 10.1109/MNET.102.2100394
![]() |
[3] |
S. Xia, Z. Yao, Y. Li, S. Mao, Online distributed offloading and computing resource management with energy harvesting for heterogeneous MEC-enabled IoT, IEEE Trans. Wireless Commun., 20 (2021), 6743−6757. https://doi.org/10.1109/TWC.2021.3076201 doi: 10.1109/TWC.2021.3076201
![]() |
[4] |
Q. Zhang, Z. Guo, Y. Zhu, P. Vijayakumar, A. Castiglione, B. B. Gupta, A deep learning-based fast fake news detection model for cyber-physical social services, Pattern Recognit. Lett., 168 (2023), 31−38, https://doi.org/10.1016/j.patrec.2023.02.026 doi: 10.1016/j.patrec.2023.02.026
![]() |
[5] |
Z. Guo, K. Yu, A. Jolfaei, F. Ding, N. Zhang, Fuz-Spam: label smoothing-based fuzzy detection of spammers in Internet of Things, IEEE Trans. Fuzzy Syst., 30 (2021), 4543−4554. https://doi.org/10.1109/TFUZZ.2021.3130311 doi: 10.1109/TFUZZ.2021.3130311
![]() |
[6] |
L. Zhao, Z. Yin, K. Yu, X. Tang, L. Xu, Z. Guo, et al., A fuzzy logic based intelligent multi-attribute routing scheme for two-layered SDVNs, IEEE Trans. Netw. Serv. Manage., 19 (2022), 4189−4200. https://doi.org/10.1109/TNSM.2022.3202741 doi: 10.1109/TNSM.2022.3202741
![]() |
[7] |
Z. Cai, X. Zheng, J. Yu, A differential-private framework for urban traffic flows estimation via taxi companies, IEEE Trans. Ind. Inf., 15 (2019), 6492−6499. https://doi.org/10.1109/TⅡ.2019.2911697 doi: 10.1109/TⅡ.2019.2911697
![]() |
[8] |
L. Huang, R. Nan, K. Chi, Q. Hua, K. Yu, N. Kumar, et al., Throughput guarantees for multi-cell wireless powered communication networks with non-orthogonal multiple access, IEEE Trans. Veh. Technol., 71 (2022), 12104−12116. https://doi.org/10.1109/TVT.2022.3189699 doi: 10.1109/TVT.2022.3189699
![]() |
[9] |
X. Shen, G. Shi, H. Ren, W. Zhang, Biomimetic vision for zoom object detection based on improved vertical grid number YOLO algorithm, Front. Bioeng. Biotechnol., 10 (2022), 905583. https://doi.org/10.3389/fbioe.2022.905583 doi: 10.3389/fbioe.2022.905583
![]() |
[10] |
G. Shi, X. Shen, L. Gu, S. Weng, Y. He, Multipath interference analysis for low power RFID sensor under metal medium environment, IEEE Sens. J., 19 (2023) 279−287. https://doi.org/10.1109/JSEN.2023.3253571 doi: 10.1109/JSEN.2023.3253571
![]() |
[11] |
D. Peng, D. He, Y. Li, Z. Wang, Integrating terrestrial and satellite multibeam systems toward 6G: techniques and challenges for interference mitigation, IEEE Wireless Commun., 29 (2022), 24−31. https://doi.org/10.1109/MWC.002.00293 doi: 10.1109/MWC.002.00293
![]() |
[12] |
C. Zhang, G. Zhou, F. Chang, X. Yang, Learning domain ontologies from engineering documents for manufacturing knowledge reuse by a biologically inspired approach, Int. J. Adv. Manuf. Technol., 106 (2020), 2535−2551. https://doi.org/10.1007/s00170-019-04772-1 doi: 10.1007/s00170-019-04772-1
![]() |
[13] |
Z. Guo, K. Yu, Z. Lv, K. K. R. Choo, P. Shi, J. J. P. C. Rodrigues, Deep federated learning enhanced secure POI microservices for cyber-physical systems, IEEE Wireless Commun., 29 (2022), 22−29. https://doi.org/10.1109/MWC.002.2100272 doi: 10.1109/MWC.002.2100272
![]() |
[14] |
X. Zheng, Z. Cai, Privacy-preserved data sharing towards multiple parties in industrial IoTs, IEEE J. Sel. Areas Commun., 38 (2020), 968−979. https://doi.org/10.1109/JSAC.2020.2980802 doi: 10.1109/JSAC.2020.2980802
![]() |
[15] |
C. Zhang, G. Zhou, J. Li, F. Chang, K. Ding, D. Ma, A multi-access edge computing enabled framework for the construction of a knowledge-sharing intelligent machine tool swarm in Industry 4.0, J. Manuf. Syst., 66 (2023), 56−70. https://doi.org/10.1016/j.jmsy.2022.11.015 doi: 10.1016/j.jmsy.2022.11.015
![]() |
[16] |
T. Guo, X. Lu, K. Yu, Y. Zhang, W. Wei, Integration of light curve brightness information and layered discriminative constrained energy minimization for automatic binary asteroid detection, IEEE Trans. Aerosp. Electron. Syst., 58 (2022), 4984−4999. https://doi.org/10.1109/TAES.2022.3192804 doi: 10.1109/TAES.2022.3192804
![]() |
[17] |
Y. Lu, L. Yang, S. X. Yang, Q. Hua, A. K. Sangaiah, T. Guo, et al., An intelligent deterministic scheduling method for ultra-low latency communication in edge enabled industrial Internet of Things, IEEE Trans. Ind. Inf., 19 (2023), 1756−1767. https://doi.org/10.1109/TⅡ.2022.3186891 doi: 10.1109/TⅡ.2022.3186891
![]() |
[18] |
C. Jin, D. Chen, Z. Lin, Z. Liu, M. Wu, How do you visit: identifying addicts from large-scale transit records via scenario deep embedding, GeoInformatica, 25 (2021), 799−820. https://doi.org/10.1007/s10707-021-00448-9 doi: 10.1007/s10707-021-00448-9
![]() |
[19] |
H. Yang, R. Ke, Z. Cui, Y. Wang, K. Murthy, Toward a real-time Smart Parking Data Management and Prediction (SPDMP) system by attributes representation learning, Int. J. Intell. Syst., 37 (2022), 4437−4470. https://doi.org/10.1002/int.22725 doi: 10.1002/int.22725
![]() |
[20] |
F. Firouzi, B. Farahani, M. Daneshmand, K. Grise, J. Song, R. Saracco, et al., Harnessing the power of smart and connected health to tackle COVID-19: IoT, AI, robotics, and blockchain for a better world, IEEE Internet Things J., 8 (2021), 12826−12846. https://doi.org/10.1109/JIOT.2021.3073904 doi: 10.1109/JIOT.2021.3073904
![]() |
[21] |
F. Piccialli, V. S. Di Cola, F. Giampaolo, S. Cuomo, The role of artificial intelligence in fighting the COVID-19 pandemic, Inf. Syst. Front., 23 (2021), 1467−1497. https://doi.org/10.1007/s10796-021-10131-x doi: 10.1007/s10796-021-10131-x
![]() |
[22] |
A. Chakraborty, D. Mukherjee, S. Mitra, Development of pedestrian crash prediction model for a developing country using artificial neural network, Int. J. Inj. Control Saf. Promot., 26 (2019), 283−293. https://doi.org/10.1080/17457300.2019.1627463 doi: 10.1080/17457300.2019.1627463
![]() |
[23] |
A. D'Alconz, I. Drago, A. Morichetta, M. Mellia, P. Casas, A survey on big data for network traffic monitoring and analysis, IEEE Trans. Netw. Serv. Manage., 16 (2019), 800−813. https://doi.org/10.1109/TNSM.2019.2933358 doi: 10.1109/TNSM.2019.2933358
![]() |
[24] |
G. Shi, X. Shen, F. Xiao, Y. He, DANTD: a deep abnormal network traffic detection model for security of industrial Internet of Things using high-order features, IEEE Internet Things J., 20 (2023), 1−10. https://doi.org/10.1109/JIOT.2023.3253777 doi: 10.1109/JIOT.2023.3253777
![]() |
[25] |
V. Hassija, V. Saxena, V. Chamola, F. R. Yu, A parking slot allocation framework based on virtual voting and adaptive pricing algorithm, IEEE Trans. Veh. Technol., 69 (2020), 5945−5957. https://doi.org/10.1109/TVT.2020.2979637 doi: 10.1109/TVT.2020.2979637
![]() |
[26] |
K. H. de Jesus Prado, L. S. Souza, I. D. de Jesus Junior, M. C. Júnior, Applied intelligent data analysis to government data related to criminal incident: a systematic review, J. Appl. Secur. Res., 15 (2020), 297−331. https://doi.org/10.1080/19361610.2020.1716511 doi: 10.1080/19361610.2020.1716511
![]() |
[27] |
C. Jin, G. Zhang, M. Wu, S. Zhou, T. Fu, Textual content prediction via fuzzy attention neural network model without predefined knowledge, China Commun., 17 (2020), 211−222. https://doi.org/10.23919/JCC.2020.06.017 doi: 10.23919/JCC.2020.06.017
![]() |
[28] |
P. M. Asaro, AI ethics in predictive policing: from models of threat to an ethics of care, IEEE Technol. Soc. Mag., 38 (2019), 40−53. https://doi.org/10.1109/MTS.2019.2915154 doi: 10.1109/MTS.2019.2915154
![]() |
[29] |
M. A. AlGhamdi, M. A. Khan, Intelligent analysis of Arabic tweets for detection of suspicious messages, Arabian J. Sci. Eng., 45 (2020), 6021−6032. https://doi.org/10.1007/s13369-020-04447-0 doi: 10.1007/s13369-020-04447-0
![]() |
[30] |
X. Ferrer, T. van Nuenen, J. M. Such, M. Coté, N. Criado, Bias and discrimination in AI: a cross-disciplinary perspective, IEEE Technol. Soc. Mag., 40 (2021), 72−80. https://doi.org/10.1109/MTS.2021.3056293 doi: 10.1109/MTS.2021.3056293
![]() |
[31] |
A. Zyner, S. Worrall, E. Nebot, Naturalistic driver intention and path prediction using recurrent neural networks, IEEE Trans. Intell. Transp. Syst., 21 (2019), 1584−1594. https://doi.org/10.1109/TITS.2019.2913166 doi: 10.1109/TITS.2019.2913166
![]() |
[32] |
S. Asadianfam, M. Shams, A. R. Kenari, TVD-MRDL: traffic violation detection system using MapReduce-based deep learning for large-scale data, Multimedia Tools Appl., 80 (2021), 2489−2516. https://doi.org/10.1007/s11042-020-09714-8 doi: 10.1007/s11042-020-09714-8
![]() |
[33] |
S. Raaijmakers, Artificial intelligence for law enforcement: challenges and opportunities, IEEE Secur. Privacy, 17 (2019), 74−77. https://doi.org/10.1109/MSEC.2019.2925649 doi: 10.1109/MSEC.2019.2925649
![]() |
[34] |
A. Lavorgna, S. E. Middleton, B. Pickering, G. Neumann, FloraGuard: tackling the online illegal trade in endangered plants through a cross-disciplinary ICT-enabled methodology, J. Contemp. Crim. Justice, 36 (2020), 428−450. https://doi.org/10.1177/1043986220910297 doi: 10.1177/1043986220910297
![]() |
[35] |
D. Carneiro, A. Amaral, M. Carvalho, L. Barreto, An anthropocentric and enhanced predictive approach to smart city management, Smart Cities, 4 (2021), 1366−1390. https://doi.org/10.3390/smartcities4040072 doi: 10.3390/smartcities4040072
![]() |