论文修改 Data mining and semantic web

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3.2 Using semantic dependencies to Mine Depressive Symptoms from Consultation Records

Many psychiatric Web sites have developed various psychiatric screening services for mental health care and crisis prevention that people can use these services to consult professionals about depressive symptoms, get a preliminary assessment of their symptoms' severity, and receive health education via email or other communication media. Analyzing consultation records and making suggestion with the current systems take a lot of time of professionals. Semantic web can help so much to solve this problem. The new system should has a service that first understand what kind of depressive symptoms people are experiencing and the semantic relations between symptoms; then it could offer further diagnostic and educational services. In [4] a framework is suggested for mining depressive symptoms and their relations from consultation records.

In this framework depressive symptoms are embedded in a single sentence or a discourse segment-that is, successive sentences describing the same depressive symptom. As the domain knowledge Hamilton Depression Rating Scale (HDSR) is used. Data mining methods are used to identify the symptom. The mining task is decomposed into subtasks:

  • Identify discourse segments by grouping the successive sentences with the same semantic label.
  • Discover semantic relations that hold between discourse segments.

In this framework semantic-dependency, lexical-cohesion, and domain-ontology knowledge sources are integrated to mine depressive symptoms and their relations. To identify the discourse segments, each sentence's semantic dependencies are modeled using a semantic dependency graph (SDG). In SDG head word of each sentence that is the central element to which other elements have some dependency relation, that is a relation between each word toke and its head in a sentence, is used to label sentences. SDG has semantic dependencies that provide the significant features for inferring a semantic label for each sentence. Four kind of semantic relations are discovered among the discourses:

    • Cause-effect-because, therefore
    • Contrast-however, but
    • Joint-and, also
    • Temporal sequence-before, after

The experiments in [4] shows that the framework identifies significant features for the task of mining depressive symptoms and heir semantic relations to support interactive psychiatric services. The semantic-dependency structure captures the intra sentential information, the lexical cohesion captures the inter sentential information, and the domain ontology models the domain knowledge. Integrating these knowledge sources is a promising approach to the mining task.

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