Relation extraction for manufacturing knowledge graphs based on feature fusion of attention mechanism and graph convolution network

Abstract

Relation extraction is a crucial step in the constructions of knowledge graphs (KGs). However, relation extraction is performed manually in the manufacturing field due to the sentence characteristics, which include weak correlation and high entity density. This approach has the disadvantages of low efficiency and high dependence on experts. At present, very few studies have been performed on relation extraction in the manufacturing field, so establishing a relation extraction model with high efficiency is an urgent need. Therefore, in this paper, a relation extraction model is proposed for manufacturing knowledge (MKREM), in which word embedding is obtained by the Bi-LSTM layer to improve robustness, and a Simplified Graph Convolution Network (SGC) layer is applied to quickly mine the entity information. Then, dependency and semantic features are extracted by the multi-head stacked GCN and relation attention mechanism, respectively. Finally, the dependency and semantic features are fused to generate the comprehensive features for relation extraction so that better performance on texts with weak correlation and high entity density can be obtained. The performance of MKREM is tested by experiments on the equipment maintenance dataset and the quality dataset from an automobile enterprise, and its effectiveness is verified in the automobile manufacture filed. The results show that the F1 scores obtained using MKREM are 2% higher than those of the commonly used models on both datasets, and the F1 scores when using Contextualized-MKREM are improved by 3%, so MKREM is very suitable for the automatic relation extraction during establishing manufacturing KGs.