面向数控机床设计知识图谱构建的实体识别

Entity Recognition for CNC Machine Tool Design Knowledge Graph

为解决数控(computer numerical control,CNC)机床设计知识图谱构建过程中关键实体的抽取问题,制定了数控机床领域知识分类标准和标注策略,构建了领域数据集,并提出了一种基于RoBERTa(robustly optimized BERT pretraining approach)的数控机床设计知识实体识别方法。首先,利用数控机床领域数据集对RoBERTa模型进行微调,再利用RoBERTa对文本编码,生成向量表示;其次,采用双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络提取向量特征;最后,利用条件随机场(conditional random field,CRF)推理出最优结果,进而为实体打上标签。实验结果表明:模型在数据集上的F1值为86.139%;对多数关键实体的F1值大于85%;相比其他模型提升2%~18%。可见该方法在数控机床设计知识实体识别中具有明显优势,能够识别机床设计知识文本包含的关键实体,为数控机床设计知识图谱构建提供了数据基础。

In order to solve the problem of extracting machine tool design knowledge in the construction of computer numerical control(CNC)machine tool design knowledge graph,the classification standard and labeling strategy of knowledge in the field of CNC machine tools are formulated and a domain data set was constructed.The entity recognition method based on robustly optimized BERT pretraining approach(RoBERTa)for CNC machine tool design was proposed.Firstly,RoBERTa was fine-tuned by using the data set in the field of CNC machine tools,and then the text was encoded by RoBERTa to generate a vector representation.Secondly,the bidirectional long short-term memory(BiLSTM)network was used to extract the vector features.Finally,the conditional random field(CRF)was used to infer the optimal result.The experimental results show that the F1 value of the model on the test data set is 86.139%,the F1 value of most key entity is greater than 85%.Compared with other models,it is improved by 2%~18%.It can be seen that this method has obvious advantages in the recognition of CNC machine tool design entities,and can identify the key entities in the machine tool design knowledge,providing a data basis for the knowledge graph.