## 1 Introduction  
Continuous casting refers to a continuous process of passing liquid steel through a continuous casting machine [1]. As a modern steel manufacturing technology, it is an important energy-saving part of the production process of the steel industry [2]. The continuous casting machine is mainly composed of different devices (e.g., the ladle, tundish, stopper rod, mold, and pulling machine), second cooling equipment, and cutting equipment. Continuous casting is widely used because of its high efficiency, environmental protection, energy saving, and many other advantages. Continuous casting, steelmaking,and continuous rolling have become the three carriages of progress and innovation in the iron and steel industry.  
During continuous casting, the quality of the cast slab cannot be guaranteed due to several factors such as raw material defects, improper worker operations, and unreasonable process parameter settings. Therefore, organizing and utilizing collected continuous casting quality events, along with corresponding recommendations and solutions provided by experts, can effectively take measures when quality events occur during continuous casting. In this regard, continuous casting efficiency and quality can be improved.  
Knowledge graphs (KGs) were first proposed by Google in 2012. They are graphical data structures used to describe and organize knowledge. They represent entities, concepts, and relationships in the real world as nodes and edges. The relationships between edges and nodes can map complex logic in the real world, thus achieving knowledge storage and querying. Currently, although General KGs (GKGs) cover a wide range of content, the depth of knowledge in specific domains is still insufficient [3]. Accordingly, GKGs cannot be directly applied to the quality engineering of continuous casting.  
The so-called Domain-Specific KG (DSKG) focuses on a specific domain, which can structure domain-specific knowledge and be of great significance for guiding domain-specific work. Currently, DSKGs have been extensively studied in fields such as power grids [4] and medicine [5; 6; 7], but there is little research on the quality engineering of continuous casting. Therefore, we propose a method for constructing a DSKG for quality engineering of continuous casting. It utilizes the advantages of Deep Learning (DL) in Natural Language Processing (NLP) to extract knowledge from unstructured text in the continuous casting domain, and ultimately constructs a high-quality DSKG for quality engineering of continuous casting through knowledge alignment. The contributions of this work are two-fold.  
* A joint knowledge extraction method for quality engineering of continuous casting based on self-attention partition and recombination.
* A knowledge alignment method for continuous casting based on semi-supervised incremental learning and adversarial training.  
The following contents of this paper include the following: Section 2 introduces the related works of knowledge extraction, knowledge alignment, and DSKG. Section 3 describes the construction of the DSKG for quality engineering of continuous casting. Section 4 introduces a Self-Attention Partition and Recombination Model (SAPRM) to achieve joint extraction of entity and relation for quality engineering of continuous casting, and a knowledge alignment method for continuous casting based on semi-supervised incremental learning. Section 5 presents the experiment and analyzes the results. Section 6 concludes our work.