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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (3): 29-37    DOI: 10.11925/infotech.2096-3467.2017.03.04
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Extracting and Visualizing Knowledge Graph Schema from Linked Data with Cytoscape Platform
Ying Jiang(),Jing Zhang,Lingxuan Zhu
School of Management, Beijing Normal University, Zhuhai, Zhuhai 519087, China
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Abstract  

[Objective] This paper proposes a new method to generate knowledge graph schema, aiming to help us understand the data structure before submitting a query, and improve the perfornamce of linked data retrieval. [Methods] First, we searched knowledge relations of the linked data through SPARQL. Second, we constructed knowledge graph schema triples for each identified relation. Finally, we extracted graphs schema triples from every knowledge class and merged them with those of the relations. [Results] A Cytoscape plugin was developed based on the proposed method to visualize the knowledge graph schema. [Limitations] Our method could not extract knowledge from complex classtification, such as anonymous nodes. [Conclusions] The proposed method was examined with biomedical data for single, inclusive, and bridge extractions. It could retrieve information effectively, and does not need additional crawling and index efforts.

Key wordsLinked Data      Knowledge Graph Schema      SPARQL      Cytoscape     
Received: 18 January 2017      Published: 20 April 2017

Cite this article:

Ying Jiang,Jing Zhang,Lingxuan Zhu. Extracting and Visualizing Knowledge Graph Schema from Linked Data with Cytoscape Platform. Data Analysis and Knowledge Discovery, 2017, 1(3): 29-37.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.03.04     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I3/29

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