基于关联数据的类簇语义揭示模型研究

Identifying Semantic Relations of Clusters Based on Linked Data

【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] This paper introduces a model to identify the semantic relations for the co-word analysis results based on linked data. [Methods] First, we used Google Scholar, Springer and CNKI to retrieve the literature of the related research. Then, we analyzed the clusters relations of them. Finally, we constructed and examined the semantic relation model for clusters based on the linked data graph structure. [Results] The linked data helped us effectively explore the potential semantic relations among keywords. [Limitations] Due to the limits of the collected linked data, we only identified some sematic relationship, such as hierarchical, simple relavent, as well as classes-instance ones. More research is needed to improve the quality of linked data. [Conclusions] The proposed model could successfully discover the semantic relations among keywords, which help us get more insights from the cluster analysis.