## 3 Proposed Method
### Schema Definition and Annotation Guidelines
This visual representation demonstrates how the proposed method for constructing a knowledge graph can be applied to capture the complexities of mental health issues. This section details the triple extraction process and the methods for constructing and utilizing the knowledge graph. It highlights the methodological contributions of this study by demonstrating how data structuring can be applied across various fields, particularly for managing complex medical information. Figure 2: Impact of entity linking on information extraction for major depressive disorder. ### Structure of the Knowledge Graph

As a result of the zero-shot information extraction process proposed in this study, a medical knowledge graph was constructed by systematically categorizing complex depression symptoms and relationships. This graph encompasses 381 nodes and 505 relationships and provides a detailed representation of the clinical characteristics of depression. The knowledge graph comprises two primary node types: subject and object. * Subject nodes: These nodes represent specific medical conditions or pathological states. They categorize various forms of depression according to the DSM-5 criteria, including major depressive disorder, persistent depressive disorder, premenstrual dysphoric disorder, and substance/medication-induced depressive disorder. Each subject node is labeled with the name of the disorder and a unique identifier. * Object nodes: These nodes depict the symptoms or characteristics that a subject node may exhibit. Examples include emotional or behavioral responses such as depressed mood, fatigue, and loss of interest or pleasure. Each object node includes the name of the symptom and a unique identifier. * Interaction between nodes: Within the knowledge graph, each subject node is linked to one or more object nodes that represent the manifestation of disease traits or symptoms. For instance, the \"manifests as\" relationship indicates how major depressive disorder might manifest as irritability, while the \"is characterized by\" relationship suggests that it may be characterized by a depressed mood. This structured organization and defined relationships allow for an in-depth analysis and understanding of the various symptoms of depression and their interactions. Researchers, physicians, and treatment specialists can use this information to gain a deeper understanding of the causes, manifestations, and characteristics of the diseases. This information will enable the formulation of more effective diagnostic and treatment plans. Table 1 displays a subset of the data used in the knowledge graph, encompassing 488 unique relationships. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Subject** & **Object** & **Relation** \\ \hline
1 & Major depressive disorder & Irritability & manifests as \\
2 & Major depressive disorder & Depressed mood & manifests as \\
3 & Major depressive disorder & Loss of interest or pleasure & manifests as \\
4 & Major depressive disorder & Changes in sleep patterns & manifests as \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of relationships in depressive disorders. Figure 3: Illustrative representation of the constructed knowledge graph comparing major depressive disorder and disruptive mood dysregulation disorder. ## 4 Experiments

This section details the evaluations to assess the performance of the proposed approach. Two primary experiments were performed. * Zero-shot information extraction on healthcare datasets: This experiment evaluated the effectiveness of zero-shot information extraction on various biomedical datasets. * Zero-shot relationship extraction at the document level: This experiment focused on extracting relationships from unstructured data at the document level using a zero-shot approach. ### Datasets and Settings

We employed several biomedical datasets to evaluate the effectiveness of zero-shot information extraction from healthcare data.