# A Novel Dual-Strategy Approach for Constructing Knowledge Graphs in the Home Appliance Fault Domain
## 2 Background
This approach facilitates the integration of multi-source spatio-temporal data and enhances the effectiveness of disaster prediction. While these methods can construct knowledge graphs in specific domains to some extent, they are limited by their strong data dependency and poor generalization capabilities ([PERSON] et al., 2019). These methods require substantial data annotation, and high-quality annotated data is not easily accessible in many domains ([PERSON] et al., 2019). With the development of deep learning technologies, pre-trained language models (such as BERT, Turing NLG, etc.) have demonstrated their powerful capabilities in natural language processing (NLP) tasks ([PERSON] et al., 2014; [PERSON] et al., 2015; [PERSON] et al., 2016). These pre-trained models capture common language representations from massive texts through self-supervised learning, providing a transferable semantic understanding basis for downstream tasks. It also provides help for the scarcity of data in specific fields when building the task of a knowledge map. In recent years, LLMs such as ChatGPT-4o, ChatGLM4, and Qwen-7B-Chat, through large-scale pre-training and fine-tuning, have not only achieved significant results in general NLP tasks but have also shown potential in domain-specific applications. This provides a new approach to knowledge graph construction ([PERSON] et al., 2020). For instance, [PERSON] et al. (2020) developed an intelligent compliance checking method for construction schemes by integrating knowledge graphs and LLM. They built a multi-dimensional, multi-granular knowledge graph to support domain-specific knowledge for the LLM and introduced a parsing module using text classification and entity extraction. This facilitated effective domain knowledge integration and enhanced the application of LLMs and knowledge graphs in construction industry text compliance checks. [PERSON] et al. [10] embedded knowledge graphs into large language models to create a knowledge-enhanced joint model, utilizing the graph structure of large-scale data in knowledge graphs. By further optimizing the LLM, the computational burden was alleviated, leading to successful fault diagnosis in the aerospace equipment field. LLMs, with their advantages of not requiring extensive labeled data and strong generalization capabilities, can overcome the limitations of traditional knowledge extraction methods. However, their performance in handling entities with complex boundaries is suboptimal, and the design of prompts significantly influences the quality of model output. Therefore, aiming at the problems of fuzzy entity boundary, entity nesting, and small datasets in the field of household appliance fault data, this paper designs a set of models. The model combines the high domain adaptability of deep learning in the task of named entity recognition and the strong semantic understanding ability of LLMs, uses the top-down method to construct the fault knowledge map of household appliances, and then designs a progressive prompt method to solve the challenges faced by deep learning, such as the need for high-quality data samples, the weak ability to deal with complex relationships, and the limited ability to understand the context. This method provides an idea for the construction of a knowledge map in the field of fault diagnosis in the field of household appliances. It also lays a foundation for the design of intelligent question answering and intelligent diagnosis applications based on a household appliance fault knowledge map, thus promoting the service-oriented transformation of the household appliance manufacturing industry.