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数据分析与知识发现  2017, Vol. 1 Issue (4): 57-66     https://doi.org/10.11925/infotech.2096-3467.2017.04.07
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于关联数据的类簇语义揭示模型研究
崔家旺1,2(), 李春旺1
1中国科学院文献情报中心 北京 100190
2中国科学院大学 北京100049
Identifying Semantic Relations of Clusters Based on Linked Data
Cui Jiawang1,2(), Li Chunwang1
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2University of Chinese Academy of Sciences, Beijing 100049, China
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摘要 

【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。
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崔家旺
李春旺
关键词 ： 关联数据,  共词聚类,  类簇,  语义揭示模型    
Abstract：

[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 words： Linked Data    Co-word Cluster Analysis    Cluster    Semantic Relations Revealing Model
收稿日期: 2017-02-16      出版日期: 2017-05-24
ZTFLH:  G25  
引用本文:   
崔家旺, 李春旺. 基于关联数据的类簇语义揭示模型研究[J]. 数据分析与知识发现, 2017, 1(4): 57-66.
Cui Jiawang,Li Chunwang. Identifying Semantic Relations of Clusters Based on Linked Data. Data Analysis and Knowledge Discovery, 2017, 1(4): 57-66.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.04.07      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2017/V1/I4/57
  主题词节点间关联关系示意图
  间接关联示意图
  最近公共祖先节点关联示意图
  最近公共子孙节点关联示意图
  基于关联数据的类簇语义揭示框架
  关联路径数量随路径长度变化趋势
关联路径 重要性指标 类型
$\{<\text{Cloning}>\xrightarrow{\text{http://dbpedia}\text{.org/ontology/wikiPageWikiLink}}\text{PCR }\!\!\}\!\!\text{ }$ 0.001 DR
$\{<\text{Cloning}>\xrightarrow{\text{wikiPageWikiLink}}$<Cloning_vector >$\xrightarrow{\text{wikiPageWikiLink}}$<PCR>} 0.00000072 IR
$\{<\text{Cloning}>\xrightarrow{\text{wikiPageWikiLink}}$<Bisulfite_sequencing >$\xrightarrow{\text{wikiPageWikiLink}}$<PCR>}
$\{<\text{Cloning}>\xrightarrow{\text{http://www}\text{.w3}\text{.org/2004/02/skos/core }\!\!\#\!\!\text{ broader}}<\text{Category:Cloning}>$
$\xrightarrow{\text{http://www}\text{.w3}\text{.org/2004/02/skos/core }\!\!\#\!\!\text{ broader}}<\text{Category:Biotechnology}>$		 0.00000072 IR
$\xleftarrow{\text{http://purlorg/dc/terms/subject}}<\text{PCR}>\}$ 0.00118999 LCAR
$\{<\text{Cloning}>\xrightarrow{\text{wikiPageWikiLink}}$< Molecular_cloning>$\xrightarrow{\text{http://purlorg/dc/terms/subject}}$
<Category:Molecular_biology>$\xleftarrow{\text{http://purlorg/dc/terms/subject}}$<PCR>}		 0.000260651 LCAR
$\{<\text{Cloning}>\xleftarrow{\text{http://purlorg/dc/terms/subject}}<\text{Category:}\ \text{Molecular }\!\!\_\!\!\text{ biology}>$
$\xrightarrow{\text{http://purlorg/dc/terms/subject}}$<PCR>}		 0.00720822 LCDR
$\{<\text{Cloning}>\xleftarrow{\text{rdf:type}}<\text{http://dbpedia}\text{.org/dbtax/Technique}>\xrightarrow{\text{rdf:type}}$<PCR>} 0.00139680 LCDR
  部分关联路径综合重要性指标计算结果
序号 属性 出现频次 含义 语义关系
1 http://dbpedia.org/ontology/wikiPageWikiLink 172 300 574 对应Wikipedia的链接信息 相关关系
2 http://www.w3.org/1999/02/22-rdf-syntax-ns#type 66 418 990 资源的标签信息 类和实例关系
3 http://www.w3.org/2002/07/owl#sameAs 40 637 907 指向同义资源 等同关系
4 http://dbpedia.org/property/wikiPageUsesTemplate 36 772 939 RDF抽取所用模版信息 相关关系
5 http://dbpedia.org/ontology/wikiPageWikiLinkText 23 809 294 Wikipedia超链接的文本信息 相关关系
6 http://purl.org/dc/terms/subject 22 673 220 资源的主题信息 类和实例关系
  DBpedia高频属性(部分)
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