# A Novel Dual-Strategy Approach for Constructing Knowledge Graphs in the Home Appliance Fault Domain
## 2 Background  
The concept of knowledge graphs was first introduced by Google in 2012 in a paper titled \"Knowledge Graph: Things, Not Strings\", with the goal of enhancing the understanding and information organization capabilities of search engines [4]. At its core, a knowledge graph is a structured semantic knowledge base that describes concepts in the physical world and their interrelations through symbolic representations [5]. A knowledge graph is composed of multiple \"entity-relationship-entity\" triplets, along with entity attributes and values. Due to its structured data, depth in semantic understanding, and inherent inferential capabilities, knowledge graphs have found widespread applications across various domains, including healthcare, automotive, and water resources [6; 7; 8], as well as in intelligent question answering [9] and decision support systems [10]. In the field of fault diagnosis, existing research has demonstrated the potential of knowledge graphs in data correlation analysis. [PERSON] et al. [11] constructed a knowledge graph for aircraft fault diagnosis by implementing a Start Position and Label-knowledge Enhanced Representation (SP-LEAR) entity recognition layer combined with a BERT-Convolution-Pooling relation extraction layer. Nevertheless, the effectiveness of short sentence entity recognition remains limited when dealing with entities that have vague boundaries. [PERSON] et al. [12] embeddedknowledge graphs into large language models to construct a knowledge-enhanced joint model. This model incorporated subgraph embedding learning and supplemented professional domain knowledge to facilitate fault diagnosis in the aviation assembly process. The training data only contains 200 fault cases, and although the accuracy is high, small samples may mask the performance degradation of the model in larger or more complex scenarios. [PERSON] et al. (2019) proposed a multimodal knowledge graph (MKG) construction approach that incorporates time series vibration signals, spectral data, and descriptive text from datasets to fully capture the features and interrelationships of bearing faults. Furthermore, they designed a fault diagnosis method utilizing a Relation Cascade Graph Attention Network (RC-GAT)-based MKG completion model, achieving efficient and accurate diagnosis of bearing faults. However, its application is confined solely to fault diagnosis within the bearing industry. Therefore, it is domain-dependent. Traditional knowledge mapping methods mostly rely on rule-based methods and deep learning algorithms, but they still have limitations when dealing with fuzzy entity boundaries, small amounts of data, and large-scale and diversified data in specific fields. Rule-based approaches typically require deep involvement from domain experts. Since they depend on the prior definition of domain knowledge, these methods struggle to adapt to dynamic scenarios and unseen new entities and relationships. Furthermore, while traditional deep learning methods have made progress in tasks such as named entity recognition and relationship extraction, the models' generalization capabilities remain limited. This is particularly evident when dealing with complex domain knowledge, where there is often a trade-off between accuracy and efficiency. For instance, [PERSON] et al. (2018) developed a triple extractor by formulating domain-agnostic entity-relation extraction rules based on dependency relations and part-of-speech (POS) information. They also proposed a multimodal knowledge graph (MKG) to extract knowledge from the unstructured text surrounding web images. [PERSON] et al. (2019) integrated remote sensing, geographic information, and expert knowledge by utilizing a common semantic ontology and a unified spatio-temporal framework. They combined these with multi-aspect relevant data from remote sensing technology for disaster analysis. Consequently, they constructed a knowledge graph and represented the disaster prediction model in the form of knowledge formulations.