The Construction of Knowledge Graphs in the Aviation Assembly Domain Based on a Joint Knowledge Extraction Model

The aviation assembly domain, which is a complex system, involves the multi-dimensional information of parts, processes, tools, plants and operation projects. In order to assist the knowledge management from natural language text in the aircraft manufacturing process, this paper proposes the corresponding ontology scheme and the joint knowledge extraction model, which is necessary for construct the knowledge graph in the aviation assembly domain. The model is able to automated end-to-end construct knowledge graph. The proposed model, which is based on reinforcement learning approach and a novel labeling scheme, takes the constraint relationships between entities and relations as an important identification basis. The model does not rely on manual feature setting, while it greatly reduces the training cost. The proposed joint knowledge extraction model was testified from the practical scenarios of the general assembly and component assembly. The experimental results showed that the proposed model showed an excellent performance in the aviation assembly domain, with the F1-score of 89.71% for entities, the F1-score of 91.27% for relations, and the overall average F1-score of 82.41%. Based on the superior performance of the model, the knowledge graph of the general assembly and component assembly, which included 1, 308 pairs of triples composed of five kinds of entities and three kinds of relations, was further constructed in the aviation assembly domain.