aiqbench analysis
This research demonstrates that enriching Graph Neural Networks with external semantic knowledge from sources like Wikidata significantly improves traffic forecasting accuracy beyond what physical topology alone can achieve. By fusing general-purpose knowledge graphs with sensor data, the study provides a novel pathway to enhance both prediction precision and model interpretability without altering core GNN architectures.
General Semantic Knowledge Infusion for Spatio-Temporal Traffic Forecasting
arXiv:2608.17440v1 Announce Type: new Abstract: Although Graph Neural Networks (GNNs) have made significant advances in spatio-temporal traffic forecasting, their performance is limited when they rely solely on sensor proximity or road-network topology. This paper presents a spatio-temporal prediction framework, developed to incorporate knowledge in various forms. This framework aims to improve sensor-level, contextual understanding of the environment. A general-purpose knowledge graph (e.g., Wikidata) is used to create semantic subgraphs around traffic sensors and generate knowledge graph embeddings that capture meaningful relationships, such as nearby points of interest, administrative hierarchies, and the functional roles of locations. These embeddings are then fused with conventional traffic sensor graphs to provide additional adjacency matrices informed by semantics. This allows GNNs to learn the semantic context beyond physical connectivity. This study differs from previous research in two key ways. Firstly, rather than proposing a novel GNN architecture, it demonstrates the general impact of external knowledge on prediction accuracy. Secondly, experiments with well-established traffic forecasting approaches show that external knowledge provides additional information that street network data alone cannot convey. The results show that integrating data from general-purpose knowledge graphs and sensor networks through data fusion can enhance the prediction accuracy of traffic forecasting models, and offers a potential pathway toward improved interpretability.
Abstract:Although Graph Neural Networks (GNNs) have made significant advances in spatio-temporal traffic forecasting, their performance is limited when they rely solely on sensor proximity or road-network topology. This paper presents a spatio-temporal prediction framework, developed to incorporate knowledge in various forms. This framework aims to improve sensor-level, contextual understanding of the environment. A general-purpose knowledge graph (e.g., Wikidata) is used to create semantic subgraphs around traffic sensors and generate knowledge graph embeddings that capture meaningful relationships, such as nearby points of interest, administrative hierarchies, and the functional roles of locations. These embeddings are then fused with conventional traffic sensor graphs to provide additional adjacency matrices informed by semantics. This allows GNNs to learn the semantic context beyond physical connectivity. This study differs from previous research in two key ways. Firstly, rather than proposing a novel GNN architecture, it demonstrates the general impact of external knowledge on prediction accuracy. Secondly, experiments with well-established traffic forecasting approaches show that external knowledge provides additional information that street network data alone cannot convey. The results show that integrating data from general-purpose knowledge graphs and sensor networks through data fusion can enhance the prediction accuracy of traffic forecasting models, and offers a potential pathway toward improved interpretability.
Machine Learning (cs.LG)
arXiv:2608.17440 [cs.LG]
(or arXiv:2608.17440v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.17440 arXiv-issued DOI via DataCite (pending registration)
M. t. Straten, et al. "General Semantic Knowledge Infusion for Spatio-Temporal Traffic Forecasting," 2026 27th IEEE International Conference on Mobile Data Management (MDM), Athens, Greece, 2026, pp. 44-51
https://doi.org/10.1109/MDM71479.2026.00016 DOI(s) linking to related resources
Full text under the publisher's open licence · 254 words
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