Entity relationship extraction method based on dependency parsing and graph neural networks

To support campus security governance, especially campus traffic safety management, many ternary extraction techniques in knowledge graphs rely on character-level text analysis; however, the differences between word semantics and overall word meanings often lead to ambiguity and overlap in ternary extraction. Traditional methods are difficult to effectively manage overlapping relationships in text, which seriously damages the flexibility and extraction accuracy of the dataset. In addition, entity separation significantly affects entity relationship triplet extraction, and weak associations between remote entities often blur entity boundaries and reduce recall rates. This study offers the MGRel entity relationship extraction model, which integrates dependent syntactic analysis with a graph neural network to address the issues above and enhance knowledge acquisition for campus security scenarios. Firstly, by incorporating the dependent syntactic parser alongside the dual analysis mechanism of global semantic dependency and syntactic dependency, it effectively captures long-distance semantic associations and enhances entity relationship recognition accuracy; secondly, it devises the architecture of a hierarchical semantic graph convolutional neural network to facilitate the fine-grained extraction of deep implied semantic features among entities; finally, the attention-driven multi-feature fusion module is presented to improve the discriminative capacity of the ternary classifier via a noise filtering approach. The experimental results on three core general-purpose benchmark datasets-NYT, WebNLG, and DuIE-show that the F1 score of this model increases by 1.3, 0.4 and 3.2%, respectively, compared with the current optimal model, demonstrating a considerable advantage over the comparative techniques and potential value for campus security-oriented campus traffic safety applications.