Depression is a complex mental health condition that can affect many aspects of a person’s life, including how they feel, think, and handle daily activities. The etiology of depression involves a complex interplay of genetic, biological, environmental, and psychological factors. Cognitive impairment is a common and significant feature of depression, affecting various domains of cognitive functioning. The relationship between cognitive impairment and depression is complex, with each condition potentially exacerbating the other. Knowledge Graphs have become an important AI approach to integrating various types of complex knowledge and data resources. We have constructed Knowledge Graphs of Depression. It integrates a wide range of knowledge resources related to depression, including metadata of medical literature, and their semantic annotations with well-known medical terminologies/ontologies such as SNOMED CT and UMLS. It provides a basic integration foundation of knowledge and data concerning depression for a comprehensive analysis. In this paper, we will show how Knowledge Graphs can be used for exploring the relationship between depression and cognitive impairment by literature mining and gain a comprehensive analysis of the cognitive impairment problems of depression patients. Furthermore, we provide several clinical case studies with the findings of the cognitive impairment of depression patients.