## 2 Related Work  
Knowledge graph construction refers to the process of extracting key information from documents and systematically presenting knowledge resources and their interrelationships using intelligent processing and visualization techniques [20]. Among these, information extraction is the core component of knowledge graph construction. Efficient information extraction technologies can accurately convert unstructured data into structured forms, providing strong support for subsequent graph representation [21]. Commonly used information extraction techniques include rule-based approaches, as well as machine learning and deep learning methods [22]. [PERSON] et al. [23] proposed a novel information extraction method called QuantityIE, which utilizes syntactic features from constituent parsing and dependency parsing to establish rules for structuring numerical information. The method also employs a QuantityIE-based numerical information retrieval approach to answer related questions, demonstrating its effectiveness. [PERSON] et al. [24] introduced a structure-function knowledge extraction method based on AskNature, which combines keyword extraction with dependency parsing rules. This method was applied to fan noise reduction design, showcasing its potential in extracting structure-function knowledge. [PERSON] et al. [25] addressing the challenges of managing and querying large-scale heterogeneous book data from multiple sources, constructed extraction rules based on the semi-structured characteristics of the textual data. Their practical application revealed significant improvements in management and query efficiency compared to traditional methods. However, rules are typically manually written based on domain-specific features, and as the dataset grows in size and complexity, writing and maintaining these rules become exceedingly difficult [26]. Additionally, due to the inherent ambiguity and polysemy of natural language, rule-based information extraction methods face considerable limitations when handling complex linguistic phenomena [27]. Several studies have begun utilizing machine learning/deep learning approaches for information extraction from text data to address the aforementioned issues. [PERSON] et al. [28] proposed a deep neural network model combining Long Short-Term Memory (LSTM) networks with Conditional Random Fields (CRF) for extracting semantic and syntactic information from regulatory documents. The model demonstrated superior performance in evaluation metrics such as accuracy, recall, and F1 score. [PERSON] et al. [29] introduceda deep neural network model called BBMC which integrates BERT, BiLSTM, a multi-head attention mechanism (MHATT), and CRF to extract semantic and syntactic information from biomedical texts. Experiments showed that this model excelled in accuracy, recall, and F1 score, with particular advantages in recognizing low-frequency entities, polysemy, and abbreviations. These methods partially alleviate issues such as the large volume of requirement documents and frequent changes in requirements. However, their application still faces limitations. For instance, the construction of these models relies on extensive and labor-intensive data annotation, which demands high-quality annotations, and the model training process is time-consuming [30]. Furthermore, the effectiveness of these models in extracting complex texts [31] and nested entities remains an area for improvement [32]. In recent years, large language models (LLMs), based on the Transformer framework with billions of parameters [33], have demonstrated remarkable potential in natural language processing tasks, such as natural language understanding, through extensive pre-training, enabling them to perform such tasks without initial work [34]. [PERSON] et al. [35] compared the performance of GPT-4 and ChemDataExtractor in extracting bandgap information in materials science.