# A Novel Dual-Strategy Approach for Constructing Knowledge Graphs in the Home Appliance Fault Domain
## 5 Conclusions and Discussion  
This paper proposes a dual-strategy progressive knowledge extraction method for constructing a home appliance fault knowledge graph, effectively addressing challenges such as complex text formats and severe entity nesting in fault repair cases. By developing  
Figure 8: Knowledge graph application and visualization display.  
the RoBERTa-zh-BiLSTM-Attn-CRF model, we leverage the maturity and efficiency of machine learning in NER tasks to accomplish entity extraction. Meanwhile, we utilize the high semantic awareness and strong generalization capabilities of LLMs to achieve knowledge extraction through progressive prompt design, and employ Neo4j for visualization, realizing the automated construction of a home appliance fault knowledge graph.  
Experimental results demonstrate that the proposed RoBERTa-zh-BiLSTM-Attn-CRF model achieves high precision and recall in NER tasks, successfully identifying entities from unstructured fault repair cases while effectively resolving issues such as ambiguous entity boundaries and nested entities. Additionally, LLMs exhibit promising performance in relation extraction tasks, with the ChatGLM4-plus model outperforming traditional relation extraction models. This approach reduces reliance on extensive data annotation and high-quality labeled data, thereby lowering annotation costs and improving generalization in relation extraction. These findings provide a novel solution for constructing knowledge graphs in the home appliance fault domain.  
Compared to existing methods, the proposed dual-strategy model demonstrates significant advantages. Unlike traditional rule-based approaches, our method eliminates dependence on manually defined rules. Furthermore, compared to conventional end-to-end deep learning methods, the introduction of LLMs reduces the demand for large-scale annotated data. Specifically, our approach improves the F1-score in relation extraction by 4.99 percentage points over the traditional Bert-BiLSTM-CRF model, representing a notable practical advancement.  
However, this study has certain limitations. First, the model's accuracy may decline when processing highly colloquial or non-standard texts, particularly in extracting entities such as \"solution.\" Second, the RoBERTa-zh-BiLSTM-Attn-CRF model and ChatGLM4-plus require substantial computational resources, which may hinder deployment in resource-constrained environments. Additionally, despite its strong performance, ChatGLM4-plus occasionally generates redundant or incorrect relations due to the hallucination phenomenon common in LLMs.  
Future research will focus on the following directions: (1) exploring subgraph embedding or multimodal knowledge graph algorithms to improve entity disambiguation and relation extraction accuracy; (2) investigating data augmentation techniques to expand domain-specific datasets and enhance model robustness; and (3) applying the proposed model to construct a large-scale home appliance fault knowledge graph to support downstream applications such as intelligent Q&A systems.  
Conceptualization and methodology, D.Z.; data curation and formal analysis, D.Z.; experiments and analysis, D.Z., J.Z., Y.J. and M.L.; investigation, D.Z.; validation and visualization, D.Z., Y.J. and M.L.; writing--original draft preparation, D.Z., Y.J. and M.L.; writing--review and editing, Y.J. and M.L.; resources and supervision, J.Z.; funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China under Grant no. 2021 YFF0901303.  
Data Availability Statement: Data will be available upon reasonable request.  
The authors declare no conflicts of interest.