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【目的】调研基于关联数据揭示类簇内主题词间语义关系的模型和技术方法。【方法】利用Google Scholar、Springer、CNKI等检索与研究主题相关的文献,调研分析并梳理当前类簇分析和语义关系揭示相关研究,构建基于关联数据的类簇语义关系揭示模型,通过实验验证模型的有效性。【结果】实验结果表明,利用关联数据可以有效揭示主题词间语义关系,弥补传统共词聚类分析在语义方面的不足。【局限】受实验数据限制,目前揭示出的语义关系局限于上下位类关系、类与实例关系和相关关系等类型,未考虑关联数据质量问题对语义揭示结果造成的影响。【结论】提出的基于关联数据的类簇语义关系揭示模型可以有效揭示主题词间语义关系,为共词聚类结果的理解和分析提供一种新的方式。
【Objective】 To investigate the model and technical method of revealing the semantic relations among the keywords in a cluster based on the correlation data. 【Method】 Using the literature about Google Scholar, Springer, CNKI and other retrieval and research topics, the research on current cluster analysis and semantic relationship discovery was investigated and analyzed, and the disclosure model of semantic relations based on association data was constructed. The experimental verification model Effectiveness. 【Result】 The experimental results show that the use of related data can effectively reveal the semantic relationship between the keywords and make up for the lack of semantics in the traditional co-word clustering analysis. [Limitations] Due to the limitations of experimental data, the semantic relationships currently disclosed are limited to the types of upper and lower classes, the relationship between classes and instances, and the correlation between them, and do not consider the influence of related data quality on the results of the semantic disclosure. 【Conclusion】 The proposed cluster-based semantic relationship revealing model based on association data can effectively reveal the semantic relations among the thematic terms and provide a new way for the understanding and analysis of the co-word clustering results.