## 3 Proposed Method  
This section describes each component of the proposed framework and the data processing in detail. This process includes the schema definition and annotation guideline setting, information extraction and entity linking, guideline-based model usage, triple extraction, final output, and knowledge graph structure.  
We propose a novel zero-shot approach that integrates medical knowledge graphs and LLMs to enhance information extraction from mental health disorder data. The data processing pipeline of the proposed framework is illustrated in Figure 1. To improve the extraction efficiency, we use LLMs to extract the desired information from a document corpus and identify and link relevant entities based on schema definitions and annotation guidelines. For zero-shot extraction, we provide high-quality gold annotations to a guideline-based model using the results of information extraction and entity linking, and convert them into a final knowledge graph through triple extraction. Therefore, the proposed framework focuses on effectively automating and optimizing complex data processing tasks.  
Methodology and Techniques  
Our zero-shot approach includes the following key steps:  
Schema definition involves defining entity and relationship types to ensure structured and consistent data processing. We use Python @dataclass to define these entities and relationships, which clarifies the structure and maintains consistency in data processing. The creation of annotation guidelines involves writing natural language guidelines that the model follows during information extraction. These guidelines ensure consistency and accuracy in the extracted data.  
In the information extraction phase, we use the gpt-3.5-turbo-instruct model [24] to extract entities and relationships from the input documents. This model excels at understanding the context and identifying the appropriate entities and relationships. The extracted entities are then standardized and integrated during the entity linking phase to maintain consistency. This process resolves issues of varied expressions for the same concept and converts the data into a unified format.  
The guideline-based model application involves processing the standardized data according to the established guidelines using the GoLLIE model [25]. The GoLLIE model, trained with guideline-based learning, ensures consistent data processing and enhances  
Figure 1: Guideline-Based Model Pipeline for Zero-Shot Information Extraction and Entity Linking.  
data accuracy and consistency by leveraging high-quality gold annotations. This model improves the reliability of the analysis results by ensuring data consistency and accuracy.  
In the triple extraction and final output generation phase, we utilize the results from the GoLLIE model to generate triples using the standardized entities and relationships. These triples are then converted into a knowledge graph using visualization tools and databases. This final knowledge graph visually represents complex data relationships, aiding researchers and healthcare professionals in quickly accessing the required information.  
By integrating these steps, our approach enables precise and automated processing of unstructured text data, particularly in the mental health domain, maximizing the utility of the zero-shot approach.