## 3 Proposed Method
### Schema Definition and Annotation Guidelines
The extracted triples are as follows:

* (\"Major Depressive Disorder\", \"requires\", \"patient experiences profound sadness\")
* (\"Major Depressive Disorder\", \"requires\", \"patient experiences a loss of interest\")
* (\"Major Depressive Disorder\", \"requires\", \"patient experiences a loss of pleasure\")
* (\"Major Depressive Disorder\", \"requires\", \"symptons last at least two weeks\")

Next, similar or redundant symptoms are integrated and standardized, and the triples are reconstructed as follows:

* \"profound sadness\" is standardized to \"Profound Sadness\"
* \"loss of interest\" and \"loss of pleasure\" are integrated into \"Loss of Interest or Pleasure\"
* \"symptons last at least two weeks\" is standardized to \"At Least Two Weeks\"

Finally, new triples are constructed using the standardized terms and linked to standardized terms. The final triples are as follows:

* (\"Major Depressive Disorder\", \"manifests as\", \"Profound Sadness\")
* (\"Major Depressive Disorder\", \"manifests as\", \"Loss of Interest or Pleasure\")
* (\"Major Depressive Disorder\", \" lasts\", \"At Least Two Weeks\")

This entity linking process ensures the standardization and consistency of information, enhancing the reliability of the data and enabling more accurate data analysis. The detailed process can be visually confirmed in Figure 2.

### Guideline-Based Model

We utilize a pretrained GoLLIE model [25] to analyze the depression data. This model is strong in zero-shot information extraction using annotation guidance and can effectively handle NLP and code-based input and output, making it well-suited for processing medical data such as depression data [33]. The model was trained on large text datasets collected from various domains including news, biomedicine, and social media. Notably, the training process incorporated normalization techniques, such as dropout and batch normalization, enabling the model to learn data variations and prevent overfitting. This approach allowed the GoLLIE model to reflect real-world language-usage patterns effectively and adapt to a broader range of data. Utilizing a pretrained model facilitates swift and accurate depression data analysis, bypassing the complexities of the initial model setup and lengthy training times. The model leverages the extensive language knowledge acquired during training to identify and analyze meaningful patterns and relationships within the depression data efficiently. ### Triple Extraction and Output

Triple Extraction: The triple extraction process utilizes the gold annotations generated by the LLM model above to convert the interrelationships between the entities extracted from the document into the triple form [34]. These triples consist of three elements: subject, predicate, and object. They semantically link each piece of information to create a machine-readable representation of the relationships. For instance, the relationship between \"major depressive disorder\" (subject) and \"difficulty thinking\" (object) can be expressed as a triple with the predicate \"is characterized by\". The results of the triple extraction are stored as structured data, which can be used as the basis for knowledge graphs. The resulting knowledge graph can help researchers and healthcare professionals to access the required information rapidly by visually representing complex data relationships and making them easy to navigate. This process maximizes the value of the data and plays an important role in adding depth to medical decision-making, work, and education. Output: The extracted triples are integrated into the knowledge graph, making the information readily usable in various applications. This knowledge graph plays a crucial role in visualizing complex relationships and enabling easy access to information [35]. Figure 3 shows a sample of the knowledge graph developed in this study. It illustrates the interrelationships and characteristics of major depressive disorder and disruptive mood dysregulation disorder, as defined in the DSM-5.