| description abstract | Abstract. In the assembly process design, knowledge question–answering is a crucial scenario for promoting the sharing of knowledge resources and enhancing the process design accuracy and efficiency. Simultaneously, knowledge graph technology enables efficient semantic modeling of knowledge, allowing for more accurate capture of user semantics and intentions. This, in turn, enhances the accuracy and flexibility of knowledge question–answering. However, due to the high complexity and specialization of assembly processes, current knowledge graph question–answering for assembly processes still faces challenges, such as difficulty in understanding complex queries. In response to this, this article proposes a large language model (LLM)-enhanced knowledge graph multi-hop reasoning method for assembly process question–answering. This method decomposes the multi-hop knowledge graph question–answering task into three subtasks: LLM-tuning-based question–answering chain generation task, which transforms the question into one or more question–answering chains, multi-hop question–answering chain reasoning task, and LLM-based natural language answer generation task. Among them, a graph path-based multi-hop reasoning model for assembly processes is constructed for question–answering chain generation. This model employs a “core reasoning + attribute constraints” strategy and a task-oriented negative sample setting method to enable rapid and precise reasoning between the knowledge graph and question–answering chains. The effectiveness of the proposed method is validated through the comparative experiments with existing mature knowledge graph question–answering models. In the comparative experiments, three datasets were constructed for LLM fine-tuning and question–answering chain reasoning of multi-hop questions, and the performance index HITS@5 of 0.913 surpassed the existing mature Hete-MF model and could fully meet the process designer needs. | |
