The intelligent development of continuous casting quality engineering is an essential step for the efficient production of high-quality billets. However, there are many quality defects that require strong expertise for handling. In order to reduce reliance on expert experience and improve the intelligent management level of billet quality knowledge, we focus on constructing a Domain-Specific Knowledge Graph (DSKG) for the quality engineering of continuous casting. To achieve joint extraction of billet quality defects entity and relation, we propose a Self-Attention Partition and Recombination Model (SAPRM). SAPRM divides domain-specific sentences into three parts: entity-related, relation-related, and shared features, which are specifically for Named Entity Recognition (NER) and Relation Extraction (RE) tasks. Furthermore, for issues of entity ambiguity and repetition in triples, we propose a semi-supervised incremental learning method for knowledge alignment, where we leverage adversarial training to enhance the performance of knowledge alignment. In the experiment, in the knowledge extraction part, the NER and RE precision of our model achieved 86.7% and 79.48%, respectively. RE precision improved by 20.83% compared to the baseline with sequence labeling method. Additionally, in the knowledge alignment part, the precision of our model reached 99.29%, representing a 1.42% improvement over baseline methods. Consequently, the proposed model with the partition mechanism can effectively extract domain knowledge, cand the semi-supervised method can take advantage of unlabeled triples. Our method can adapt the domain features and construct a high-quality knowledge graph for the quality engineering of continuous casting, providing an efficient solution for billet defect issues.

A variety of cast product defects may occur in the continuous casting process. By establishing a Continuous Casting Quality Knowledge Graph (C2Q-KG) focusing on the causes of cast product defects, enterprises can systematically sort out and express the relations between various production factors and cast product defects, which makes the reasoning process for the causes of cast product defects more objective and comprehensive. However, reasoning schemes for general KGs often use the same processing method to deal with different types of relations, without considering the difference in the number distribution of the head and tail entities in the relation, leading to a decrease in reasoning accuracy. In order to improve the reasoning accuracy of C2Q-KGs, this paper proposes a model based on a two-branch reasoning network. Our model classifies the continuous casting triples according to the number distribution of the head and tail entities in the relation and connects a two-branch reasoning network consisting of one connection layer and one capsule layer behind the convolutional layer. The connection layer is used to deal with the sparsely distributed entity-side reasoning task in the triple, while the capsule layer is used to deal with the densely distributed entity-side reasoning task in the triple. In addition, the Graph Attention Network (GAT) is introduced to enable our model to better capture the complex information hidden in the neighborhood of each entity and improve the overall reasoning accuracy. The experimental results show that compared with other cutting-edge methods on the continuous casting data set, our model significantly improves performance and infers more accurate root causes of cast product defects, which provides powerful guidance for enterprise production.