Affiliation:
1. School of Intelligent Systems Engineering, Sun Yan-sen University & Guandong Provincial Key Laboratory of Intelligent Transportation Systems, Shenzhen 518107 & Guangzhou 510275, China
Abstract
Domain knowledge is gradually renovating its attributes to exhibit distinct features in autonomy, propelled by the shift of modern transportation systems (TS) towards autonomous TS (ATS) comprising three progressive generations. Knowledge graph (KG) and its corresponding versions can help depict the evolving TS. Given that KG versions exhibit asymmetry primarily due to variations in evolved knowledge, it is imperative to harmonize the evolved knowledge embodied by the entity across disparate KG versions. Hence, this paper proposes a siamese-based graph convolutional network (GCN) model, namely SiG, to address unresolved issues of low accuracy, efficiency, and effectiveness in aligning asymmetric KGs. SiG can optimize entity alignment in ATS and support the analysis of future-stage ATS development. Such a goal is attained through: a) generating unified KGs to enhance data quality, b) defining graph split to facilitate entire-graph computation, c) enhancing GCN to extract intrinsic features, and d) designing siamese network to train asymmetric KGs. The evaluation results suggest that SiG surpasses other commonly employed models, resulting in average improvements of 23.90% and 37.89% in accuracy and efficiency, respectively. These findings have significant implications for TS evolution analysis and offer a novel perspective for research on complex systems limited by continuously updated knowledge.
Publisher
Association for Computing Machinery (ACM)
Subject
Artificial Intelligence,Theoretical Computer Science
Reference56 articles.
1. Federated Learning for Healthcare: Systematic Review and Architecture Proposal
2. Knowledge Graph Construction with a
Façade
: A Unified Method to Access Heterogeneous Data Sources on the Web
3. S Diaz Benavides, Silvio Domingos Cardoso, Marcos Da Silveira, and Cédric Pruski. 2022. Dyndiff: A tool for comparing versions of large ontologies. In Proceedings of SeWebMeDa workshop at ESWC conference.
4. Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah. 1993. Signature verification using a” siamese” time delay neural network. Advances in neural information processing systems 6 (1993).
5. Future directions of intelligent vehicles: Potentials, possibilities, and perspectives;Cao Dongpu;IEEE Transactions on Intelligent Vehicles,2022