## 1 Introduction  
### _Introduction of KG Inference Technology_  
As the key link in a complex product lifecycle, the assembly process comprises a series of characteristics such as high complexity, repeatability, standardization, and quality requirements. With the continuous development of intelligent manufacturing, a great deal of knowledge regarding the assembly process lacks unified expression and organization methods, which promoted the development of assembly process knowledge graph (KG) construction technology [1]. In the assembly process KG construction, due to the fine granularity and high interaction of the assembly process, there are often issues concerning missing entities/relations and incorrect knowledge, leading to the research regarding KG inference tasks, including knowledge judgment, entity, and relation completion. Therefore,KG inference technology for KG construction has received significant attention and has become a hot research domain [2]. Due to the fact that KG utilizes the triplet form of head entity, relation, and tail entity (_h,r,t_) to express and store knowledge, current studies regarding KG inference for KG construction includes knowledge judgment [3], entity completion, relation completion [4], etc. Most existing KG inference models are oriented to the general domain and are expressed in triplet form (_h,r,t_); these can be divided into three categories: logical rules-based inference models, embedded learning-based inference models, and deep learning-based inference models. The logical rules-based inference model uses the inter-entity paths automatically obtained by the machine to fit the rules. The principle is to treat some paths as approximate rules, using these to further serve as a feature training learning model to judge whether there is a specified relation between entities [5]. Typical representatives of this model type include rule inference based on a global structure, such as the path sorting algorithm (PRA) [6] and the incomplete knowledge-based association rule mining algorithm (AMIE) [7], and rule inference based on a local structure, such as the local feature extraction algorithm (SFE) [8] and the hierarchical random walk inference algorithm (HiRi) [9]. The inference model based on logical rules exhibits high interpretability and high inference accuracy when the rules are accurate, but the problem lies in the lack of more effective methods to mine complex rules, and the method is not easy to extend. The main idea behind the embedded learning-based inference model is to design algorithms to project entities and relations into low-dimensional continuous vector space, learning the distributed semantic embedded of entities or relations [10]. Typical representatives include translation distance-based embedded learning models, such as TransE with its variants [11; 12], and semantic matching based embedded learning models, such as DistMult [13] and ComplEx. The embedded learning inference model displays the advantage of high computational efficiency, alleviating data sparsity and realizing multi-source heterogeneous information fusion, but its prediction results are probabilistic and lack interpretability. The deep learning-based inference model uses the powerful feature extraction ability of a neural network to map the feature distribution to another feature space to obtain corresponding feature representation, realizing complex semantic modeling for KG inference [14]. Typical representatives include Conv E [15], Path-RNN [16], reinforcement learning-based methods [17], etc. They exhibit innate advantages for complex problem inference, but are limited by the defects of deep learning itself and a lack of reliable interpretability of the results. In addition, some scholars also try to mix different inference models to achieve complementary advantages.