Unveiling the Railway Traffic Knowledge in Tibet: An Advanced Model for Relational Triple Extraction

Abstract

To address the deficiency of existing relation extraction models in effectively extracting relational triples pertaining to railway traffic knowledge in Tibet, this paper constructs a Tibet Railway Traffic text dataset and provides an enhanced relation extraction model. The proposed model incorporates subject feature enhancement and relational attention mechanisms. It leverages a pre-trained model as the embedding layer to obtain vector representations of text. Subsequently, the subject is extracted and its semantic information is augmented using an LSTM neural network. Furthermore, during object extraction, the multi-head attention mechanism enables the model to prioritize relations associated with the aforementioned features. Finally, objects are extracted based on the subjects and relations. The proposed method has been comprehensively evaluated on multiple datasets, including the Tibet Railway Traffic text dataset and two public datasets. The results on the Tibet dataset achieve an F1-score of 93.3%, surpassing the baseline model CasRel by 0.8%, indicating a superior applicability of the proposed model. On the other hand, the model achieves F1-scores of 91.1% and 92.6% on two public datasets, NYT and WebNLG, respectively, outperforming the baseline CasRel by 1.5% and 0.8%, which highlights the good generalization ability of the proposed model.