Knowledge extraction method for operation and maintenance texts of high-speed railway turnout
To achieve the automatic construction of the knowledge graph and provide decision-making support for intelligent operation and maintenance of high-speed railway turnout,it is necessary to use knowledge extraction technology to extract key knowledge from high-speed railway turnout maintenance texts.At the same time,to further solve the problem of entity nesting and overlapping knowledge triplets in these texts,this article proposed a knowledge extraction model RTOM-KE for high-speed railway turnout operation and maintenance based on multi-module joint learning.Firstly,based on the defined entity and relation types,a two-stage knowledge labeling strategy based on BIOES was proposed to label the head entity and corresponding tail entity under the relation.Secondly,the encoding module composed of the lightweight pre-training BERT-base model and BiLSTM neural network was used to obtain the multi-dimensional shared encoding representation of the text.The hidden state of the encoding module and the global contextual features were combined as the input of the head entity extraction module.Finally,the head entity extraction module was used to extract all candidate head entities in the text.The candidate head entity labels and the multi-dimensional shared word representation from the encoding module were used as the joint input of the tail entity extraction module.The specific relation gate mechanism was used to filter the tail entities associated with the head entity to obtain the knowledge triplet of the high-speed railway turnout maintenance.Through sufficient comparative experiments and ablation experiments,the results are drawn as follows.The RTOM-KE model can accurately and comprehensively extract triplets of different complexities and effectively solve the problems of entity nesting and triplet overlapping.The Precision,Recall,and F1 values of RTOM-KE model based on the turnout operation and maintenance dataset can reach 88.3%,86.9%,and 87.6%,respectively.The research results can provide reference for further improving the knowledge extraction efficiency of more complex high-speed railway turnout maintenance texts and information extraction in other professional fields.
high-speed railwayturnoutoperation and maintenanceknowledge extractionBERT model
国家科技期刊平台
NSTL
万方数据
