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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
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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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     
Received: 16 February 2017      Published: 24 May 2017
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Jiawang Cui
Chunwang Li

Cite this article:

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

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.04.07     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I4/57

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
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
[1] 钟伟金, 李佳. 共词分析法研究(一)——共词分析的过程与方式[J]. 情报杂志, 2008, 27(5): 70-72.
[1] (Zhong Weijin, Li Jia.The Research of Co-word Analysis (1) ———The Process and Methods of Co-word Analysis[J]. Journal of Intelligence, 2008, 27(5): 70-72.)
[2] 张树良, 冷伏海. 基于文献的知识发现的应用进展研究[J]. 情报学报, 2006, 25(6): 700-712.
[2] (Zhang Shuliang, Leng Fuhai.Study on the Applicational Development of Literature-based Knowledge Discovery[J]. Journal of the China Society for Scientific and Technical Information, 2006, 25(6): 700-712.)
[3] 张晗, 任志国, 张健, 等. 基于主题词关联规则的医学文本数据库数据挖掘的尝试[J]. 医学信息学杂志, 2008, 29(1): 32-35.
[3] (Zhang Han, Ren Zhiguo, Zhang Jian, et al.Study on the Data Mining in Medical Text Database Based on Keywords Association Rules[J]. Journal of Medical Informatics, 2008, 29(1): 32-35.)
[4] 张晗, 崔雷. 生物信息学的共词分析研究[J]. 情报学报, 2003, 22(5): 613-617.
doi: 10.3969/j.issn.1000-0135.2003.05.018
[4] (Zhang Han, Cui Lei.Study of Bioinformatics through Co-word Analysis[J]. Journal of the China Society for Scientific and Technical Information, 2003, 22(5): 613-617.)
doi: 10.3969/j.issn.1000-0135.2003.05.018
[5] Cimino J J, Barnett G O.Automatic Knowledge Acquisition from Medline[J]. Methods of Information in Medicine, 1993, 32(2): 120-130.
doi: 10.1007/BF01581301 pmid: 8321130
[6] 刘明岩. 面向语义关系发现的文本挖掘研究[D]. 南京: 南京理工大学, 2010.
[6] (Liu Mingyan.Research of Text Mining About Semantic Relation Recognition[D]. Nanjing: Nanjing University of Science and Technology, 2010.)
[7] 张小刚. 基于中医药本体的语义关系发现及验证方法[D]. 杭州: 浙江大学, 2010.
[7] (Zhang Xiaogang.Traditional Chinese Medical Ontology Based Semantic Relation Discovering and Verification [D]. Hangzhou: Zhejiang University, 2010.)
[8] 魏来. 基于在线词表的Folksonomy语义关联识别方法研究[J]. 图书情报工作, 2011, 55(5): 104-108.
[8] (Wei Lai.Research of Folksonomy Semantic Association Method Based on Online Thesaurus[J]. Library and Information Service, 2011, 55(5): 104-108.)
[9] Tiddi I, D’Aquin M, Motta E. Dedalo: Looking for Clusters Explanations in a Labyrinth of Linked Data [M]. Springer International Publishing, 2014.
[10] Taheriyan M, Knoblock C A, Szekely P, et al.Leveraging Linked Data to Infer Semantic Relations Within Structured Sources[C]// Proceedings of the 6th International Workshop on Consuming Linked Data (COLD). 2015.
[11] 李楠, 张学福. 基于关联数据的知识发现模型研究[J]. 图书馆学研究, 2013, 1: 73-77.
[11] (Li Nan, Zhang Xuefu.Research on Knowledge Discovery Based on Linked Data[J]. Researches in Library Science, 2013, 1: 73-77.)
[12] 李俊, 黄春毅. 关联数据的知识发现研究[J]. 情报科学, 2013, 31(3): 79-84.
[12] (Li Jun, Huang Chunyi.Knowledge Discovery in Linked Data[J]. Information Science, 2013, 31(3): 79-84.)
[13] 高劲松, 李迎迎, 刘龙, 等. 基于关联数据的知识发现模型构建研究[J]. 情报科学, 2016, 34(6): 10-13.
[13] (Gao Jinsong, Li Yingying, Liu Long, et al.Research on Construction of the Knowledge Discovery Model Based on Linked Data[J]. Information Science, 2016, 34(6): 10-13.)
[14] 宋丽娜. 关联数据环境下基于知识地图的隐性知识发现模型研究[D]. 武汉: 华中师范大学, 2014.
[14] (Song Lina.Research on Model of Knowledge Discovery Based on Knowledge Map Under the Environment of Linked Data [D]. Wuhan: Central China Normal University, 2014.)
[15] 刘龙. 基于关联数据的知识发现过程模型研究 [D]. 武汉: 华中师范大学, 2014.
[15] (Liu Long.Research on Model of Knowledge Discovery Process Based on Linked Data [D]. Wuhan: Central China Normal University, 2014.)
[16] Narasimha V, Kappara P, Ichise R, et al.LiDDM: A Data Mining System for Linked Data[C]// Proceedings of the 2011 Linked Data on the Web. 2011.
[17] Paulheim H, Fürnkranz J.Unsupervised Generation of Data Mining Features from Linked Open Data[C]//Proceedings of the International Conference on Web Intelligence, Mining and Semantics. 2012.
[18] Ramezani R, Saraee M, Nematbakhsh M A.Finding Association Rules in Linked Data, A Centralization Approach[C]//Proceedings of the 21st Iranian Conference on Electrical Engineering. 2013.
[19] Personeni G, Daget S, Bonnet C, et al.Mining Linked Open Data: A Case Study with Genes Responsible for Intellectual Disability [M]. Springer International Publishing, 2014.
[20] Jiang X, Zhang X, Gao F, et al.Graph Compression Strategies for Instance-Focused Semantic Mining[C]//Proceedings of the 7th Chinese Semantic Web Symposium on Linked Data and Knowledge Graph. 2013.
[21] Li K, Gao J, Guo S, et al.LRBM: A Restricted Boltzmann Machine Based Approach for Representation Learning on Linked Data[C]// Proceedings of the IEEE International Conference on Data Mining. 2014.
[22] 夏立新, 谭荧. LOD的网络结构分析与可视化[J]. 现代图书情报技术, 2016(1): 65-72.
[22] (Xia Lixin, Tan Ying.Analysis and Visualization of the LOD Network Structure[J]. New Technology of Library and Information Service, 2016(1): 65-72.)
[23] Meymandpour R, Davis J G.Linked Data Informativeness[M]. Springer Berlin Heidelberg, 2013.
[24] Kasneci G, Elbassuoni S, Weikum G.MING: Mining Informative Entity-Relationship Subgraphs[C]// Proceedings of the 18th ACM Conference on Information and Knowledge Management. 2009.
[25] Balmin A, Hristidis V, Papakonstantinou Y.Objectrank: Authority-based Keyword Search in Databases[C]// Proceedings of the 30th International Conference on Very Large Data Bases.2004.
[26] Nie Z, Zhang Y, Wen J R, et al.Object-level Ranking: Bringing Order to Web Objects[C]//Proceedings of the 2005 International Conference on World Wide Web. 2005.
[27] Ng M K P, Li X T, Ye Y M. MultiRank: Co-ranking for Objects and Relations in Multi-relational Data[C]// Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011.
[28] 蒋世银, 李春旺. 基于关联数据的科研机构评价研究述评[J]. 情报理论与实践, 2015, 38(2): 136-140.
[28] (Jiang Shiyin, Li Chunwang.Review on the Evaluation of Scientific Research Institution Based on Linked Data[J]. Information Studies: Theory & Application, 2015, 38(2): 136-140.)
[29] Bamba B, Mukherjea S.Utilizing Resource Importance for Ranking Semantic Web Query Results[C]//Proceedings of the 2nd International Conference on Semantic Web and Databases. 2004.
[30] Franz T, Schultz A, Sizov S, et al.TripleRank: Ranking Semantic Web Data by Tensor Decomposition[C]// Proceedings of the International Semantic Web Conference. 2009.
[31] Hulpus I, Prangnawarat N, Hayes C.Path-Based Semantic Relatedness on Linked Data and Its Use to Word and Entity Disambiguation[C]// Proceedings of the International Semantic Web Conference. 2015.
[32] 岳阳, 孙静, 石达友, 等. 基于共词分析的兽医分子生物学领域研究热点分析及初步展望[J]. 广东畜牧兽医科技, 2015, 40(2): 1-4.
doi: 10.3969/j.issn.1005-8567.2015.02.001
[32] (Yue Yang, Sun Jing, Shi Dayou, et al.Interpretation and Preliminary Outlook of the Research Focus in Veterinary Molecular Biology Based on the Co-word Analysis[J]. Guangdong Journal of Animal and Veterinary Science, 2015, 40(2): 1-4.)
doi: 10.3969/j.issn.1005-8567.2015.02.001
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