# A Novel Dual-Strategy Approach for Constructing Knowledge Graphs in the Home Appliance Fault Domain
For instance, terms like \"temperature controller\", \"thermostat\", and \"temperature control component\" all refer to the same concept but make it challenging to define clear entity boundaries. In existing research, knowledge graph construction methods predominantly rely on deep learning for knowledge extraction tasks [2]. Deep learning, particularly in named entity recognition (NER) tasks, leverages pre-trained models for transfer learning. This approach allows for rapid adaptation to new domains using annotated datasets, offering advantages such as low training costs and strong domain adaptability. Large language models (LLMs), on the other hand, excel in relationship extraction tasks due to their superior contextual understanding and generalization capabilities [3]. In this study, a dataset for the home appliance fault domain was constructed using manually annotated  
Figure 1: Examples of nested and discontinuous entities. maintenance records as the raw data. The efficiency of deep learning was utilized for NER to perform entity extraction. Subsequently, the semantic processing strengths of LLMs were applied to address the challenges of poor association capability, low extraction accuracy, and weak generalization in entity association and relationship extraction tasks. A fine-tuning strategy was designed for LLM-based association and extraction, which effectively enhanced model performance. This approach also improved the interpretability and transparency of the model's decision-making process. The constructed knowledge graph for the home appliance fault domain was visualized using Neo4j (Version: Community Edition 3.5.28). By uncovering relationships between entities in the domain, the knowledge graph provides valuable support for both users and technicians, enabling faster and more informed decision-making. In summary, the main contributions of this study are as follows:  
1. Ontology design and dataset construction for the home appliance fault domain: Using maintenance records and user manual data as examples, this study adopts a top-down approach to design the ontology for the home appliance fault knowledge graph. A manually annotated domain-specific dataset was constructed to facilitate the acquisition of information at the data layer of the knowledge graph. 2. Proposing a dual-strategy automated knowledge extraction method: A novel approach is introduced to extract knowledge from heterogeneous and multi-source home appliance fault datasets. For the NER task, multi-head attention mechanisms and a Chinese pre-trained model are employed to address challenges such as the ambiguity of entity boundaries. For ontology association and relationship extraction tasks, a progressive LLM-based knowledge extraction method is developed, which integrates knowledge fusion to generate the final set of triplets. The combination of these two strategies enhances the efficiency of knowledge extraction, providing a practical pathway for the integration and standardization of knowledge in the home appliance domain. Transforming textual fault repair data into a structured knowledge graph: Through ontology construction and automated knowledge extraction, this study successfully converts fault repair case texts into a clear and comprehensible knowledge graph. This will help to transform unstructured text into a structured representation. And the constructed graph can help maintenance personnel to quickly diagnose faults, thereby improving the efficiency of fault diagnosis.