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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (4): 57-66    DOI: 10.11925/infotech.2096-3467.2017.04.07
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Identifying Semantic Relations of Clusters Based on Linked Data
Jiawang Cui1,2(),Chunwang Li1
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2University of Chinese Academy of Sciences, Beijing 100049, China
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[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.

Key wordsLinked Data      Co-word Cluster Analysis      Cluster      Semantic Relations Revealing Model     
Received: 16 February 2017      Published: 24 May 2017

Cite this article:

Jiawang Cui,Chunwang Li. Identifying Semantic Relations of Clusters Based on Linked Data. Data Analysis and Knowledge Discovery, 2017, 1(4): 57-66.

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