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With the rapid growth of depressive disorders, many psychiatric Web sites have developed various psychiatric screening services for mental health care and crisis prevention. We propose a framework for mining depressive symptoms and their relations from consultation records. The records contain many kinds of depressive symptoms, such as depressed mood, suicide ideas, anxiety, sleep disturbances, and so on. The depressive symptoms are embedded in a single sentence or a discourse segment - that is, successive sentences describing the same depressive symptom. Our framework infers the semantic label according to a sentence's semantic dependencies and the HowNet knowledge base, a Chinese-language concept hierarchy that defines higher-level abstractions, or hypernyms, for Chinese words, concepts, and interconcept relations. Moreover, the framework computes the lexical cohesion between sentences to enhance its semantic labeling power and adopts a domain ontology to mine the semantic relations. Preliminary experiments show the semantic dependencies within and between sentences and the domain ontology used in this approach are significant features in the mining task.