Abstract: To address the issues of single training sample features and low accuracy in relation extraction during the current knowledge graph construction process,a Knowledge Graph based on Grid and Segments Attention Mechanism (KG-GSAM) was established.For entity recognition tasks,a lattice structure was introduced to improve the bidirectional gated recurrent neural network;for relation extraction tasks,a segment scale attention mechanism was introduced to build a relation extraction neural network.Experiments were conducted on publicly available datasets and datasets constructed from patent texts of automated guided vehicles in the past three years.The results showed that the proposed model had certain advantages over other knowledge graph construction models in terms of Precision,Recall,and F1 score

Key words: knowledge graph, grid, segment attention mechanism, BERT model, relation extraction neural network, automated guided vehicle

摘要： 为解决当前知识图谱构建模型过程中训练样本特征单一、关系抽取准确率低下的问题,建立一种基于格点网格与段尺度注意力机制的知识图谱自动构建模型(KG-GSAM)。针对实体识别任务,引入格点网格结构对双向门控循环神经网络进行改进;针对关系抽取任务,引入段尺度注意力机制,搭建关系抽取神经网络。在公开数据集和近三年自动导引车领域的专利文本构建的数据集上分别进行实验,结果表明所建立模型在Precision、Recall和F1-score三个指标上与其他知识图谱构建模型相比有一定的优越性。

关键词: 知识图谱, 格点网格, 段尺度注意力机制, BERT模型, 关系抽取神经网络, 自动导引车