Please wait a minute...
Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (11): 75-83    DOI: 10.11925/infotech.2096-3467.2017.0752
Orginal Article Current Issue | Archive | Adv Search |
Linking Knowledge Elements from Online Community
Guo Chen1(),Lu Xiao2
1School of Economics & Management, Nanjing University of Science and Technology, Nanjing 210094, China
2School of Information Management, Nanjing University, Nanjing 210023, China
Download: PDF(2620 KB)   HTML ( 1
Export: BibTeX | EndNote (RIS)      

[Objective] This paper proposes a system to link the fragmented knowledge elements from an online community, aiming to help explore knowledge more effectively. [Methods] First, we built a domain knowledge base for the online community. Then, we combined units of the domain knowledge base with the semantically similar elements of the user-generated-content (UGC). Finally, we identified the knowledge units of the UGC and linked them with relevant Web pages. [Results] We examined the proposed method with a Chinese cardiovascular BBS site. A total of 2,211 cardiovascular concepts and 5,741 fine-grained relations were extracted to create the domain knowledge base. We identified the knowledge elements from 5,020 posts automatically and linked them with relevant webpages. [Limitations] Only investigated the linking of knowledge elements at the micro level. [Conclusions] The proposed system can effectively establish connections between knowledge units and UGC documents based on the existing resource organization schemes. The new method could be used in other fields.

Key wordsOnline Community      Knowledge Organization      Domain Knowledge Base      Domain Conceptual Relation      Knowledge Element Linking System     
Received: 27 July 2017      Published: 27 November 2017

Cite this article:

Guo Chen,Lu Xiao. Linking Knowledge Elements from Online Community. Data Analysis and Knowledge Discovery, 2017, 1(11): 75-83.

URL:     OR

[1] Xu K, Chen Y, Jiang Y, et al.A Comparative Study of Correlation Measurements for Searching Similar Tags[C]// Proceedings of International Conference on Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2008: 709-716.
[2] 易明, 王学东, 邓卫华. 基于社会网络分析的社会化标签网络分析与个性化信息服务研究[J]. 中国图书馆学报, 2010, 36(2): 107-114.
[2] (Yi Ming, Wang Xuedong, Deng Weihua.A Research on the Tag Network Analysis Based on Social Network Analysis (SNA) and the Personalized Information Service[J]. Journal of Library Science in China, 2010, 36(2): 107-114.)
[3] 杨萌, 张云中, 徐宝祥. 社会化标注系统资源聚合与导航研究综述[J]. 情报理论与实践, 2014, 37(3): 140-144.
[3] (Yang Meng, Zhang Yunzhong, Xu Baoxiang.Review of Resources Aggregation and Navigation of Social Tagging System[J]. Information Studies: Theory & Application, 2014, 37(3): 140-144.)
[4] Angeletou S.Semantic Enrichment of Folksonomy Tagspaces[C]//Proceedings of International Semantic Web Conference. Springer Berlin Heidelberg, 2008.
[5] Specia L, Motta E.Integrating Folksonomies with the Semantic Web[C]//Proceedings of European Semantic Web Conference 2007: The Semantic Web: Research and Applications. 2007: 624-639.
[6] Wang L, Jia Y, Han W.Instant Message Clustering Based on Extended Vector Space Model[C]//Proceedings of the 2nd International Symposium on Intelligence Computation and Applications (ISICA 2007), Wuhan, China. 2007: 435-443.
[7] 唐晓波, 肖璐. 基于依存句法分析的微博主题挖掘模型研究[J]. 情报科学, 2015, 33(9): 61-65.
[7] (Tang Xiaobo, Xiao Lu.Research on Micro-Blog Topics Mining Model on Dependency Parsing[J]. Information Science, 2015, 33(9): 61-65.)
[8] 马慧芳, 曾宪桃, 李晓红,等. 改进的频繁词集短文本特征扩展方法[J]. 计算机工程, 2016, 42(10): 213-218.
[8] (Ma Huifang, Zeng Xiantao, Li Xiaohong, et al.Short Text Feature Extension Method of Improved Frequent Term Set[J]. Computer Engineering, 2016, 42(10): 213-218.)
[9] 李湘东, 曹环, 丁丛, 等. 利用《知网》和领域关键词集扩展方法的短文本分类研究[J]. 现代图书情报技术, 2015(2): 31-38.
[9] (Li Xiangdong, Cao Huan, Ding Cong, et al.Short-text Classification Based on HowNet and Domain Keyword Set Extension[J]. New Technology of Library and Information Service, 2015(2): 31-38.)
[10] He H, Chen B, Xu W, et al.Short Text Feature Extraction and Clustering for Web Topic Mining[C]//Proceedings of the 3rd International Conference on Semantics, Knowledge and Grid. IEEE, 2007: 382-385.
[11] 贺涛, 曹先彬, 谭辉. 基于免疫的中文网络短文本聚类算法[J]. 自动化学报, 2009, 35(7): 896-902.
[11] (He Tao, Cao Xianbin, Tan Hui.An Immune Based Algorithm for Chinese Network Short Text Clustering[J]. Acta Automatical Sinica, 2009, 35(7): 896-902.)
[12] 金春霞, 周海岩. 动态向量的中文短文本聚类[J]. 计算机工程与应用, 2011, 47(33): 156-158.
[12] (Jin Chunxia, Zhou Haiyan.Chinese Short Text Clustering Based on Dynamic Vector[J]. Computer Engineering and Applications, 2011, 47(33): 156-158.)
[13] 田博, 凡玲玲. 基于交互行为的在线社会网络社区发现方法研究[J]. 情报杂志, 2016, 35(11): 183-188.
[13] (Tian Bo, Fan Lingling.New Method of Community Detection for Online Social Networks Based on Interactive Behaviors[J]. Journal of Intelligence, 2016, 35(11): 183-188.)
[14] 孙怡帆, 李赛. 基于相似度的微博社交网络的社区发现方法[J]. 计算机研究与发展, 2014, 51(12): 2797-2807.
[14] (Sun Yifan, Li Sai.Similarity-based Community Detection in Social Network of Microblog[J]. Journal of Computer Research and Development, 2014, 51(12): 2797-2807.)
[15] 刘冰玉, 王翠荣, 王聪, 等. 基于动态主题模型融合多维数据的微博社区发现算法[J]. 软件学报, 2017, 28(2): 246-261.
[15] (Liu Bingyu, Wang Cuirong, Wang Cong, et al.Microblog Community Discovery Algorithm Based on Dynamic Topic Model with Multidimensional Data Fusion[J]. Journal of Software, 2017, 28(2): 246-261.)
[16] 曾建勋. 知识链接的研究现状与发展趋势[J]. 情报理论与实践, 2011, 34(2): 119-123.
[16] (Zeng Jianxun.Research and Development of Knowledge Linking[J]. Information Studies: Theory & Application, 2011, 34(2): 119-123.)
[17] 贺德方. 知识链接发展的历史、未来和行动[J]. 现代图书情报技术, 2005(3): 11-15.
[17] (He Defang.Knowledge Linking: History, Future and Action[J]. New Technology of Library and Information Service, 2005(3): 11-15.)
[18] 滕广青, 毕强. 知识组织体系的演进路径及相关研究的发展趋势探析[J]. 中国图书馆学报, 2010, 36(5): 49-53.
[18] (Teng Guangqing, Bi Qiang.Research and Development of Knowledge Organization System[J]. Journal of Library Science in China, 2010, 36(5): 49-53.)
[19] 王知津. 从情报组织到知识组织[J]. 情报学报, 1998, 17(3): 230-234.
[19] (Wang Zhijin.From Information Organization to Knowledge Organization[J]. Journal of the China Society for Scientific and Technical Information, 1998, 17(3): 230-234.)
[20] 姜永常, 杨宏岩, 张丽波. 基于知识元的知识组织及其系统服务功能研究[J]. 情报理论与实践, 2007, 30(1): 37-40.
[20] (Jiang Yongchang, Yang Hongyan, Zhang Libo.Research on Knowledge Organization Based on Knowledge Elements and the Service Functionality[J]. Information Studies: Theory & Application, 2007, 30(1): 37-40.)
[21] 陈果. 基于领域概念关联的网络社区知识聚合研究[D]. 武汉: 武汉大学, 2015.
[21] (Chen Guo.Research on the Knowledge Aggregation in Network Community Based on Domain Conceptual Relations[D]. Wuhan: Wuhan University, 2015.)
[22] Medelyan O, Milne D, Legg C, et al.Mining Meaning from Wikipedia[J]. International Journal of Human-Computer Studies, 2008, 67(9): 716-754.
[23] Clauson K A, Polen H H, Boulos M N, et al.Scope, Completeness, and Accuracy of Drug Information in Wikipedia[J]. Annals of Pharmacotherapy, 2008, 42(12): 1814-1821.
[24] 常春, 吴雯娜, 曾建勋. 基于后方一致获取词间关系[J]. 情报科学, 2009, 27(7): 1085-1088.
[24] (Chang Chun, Wu Wenna, Zeng Jianxun.Based on Same End in Terms to Acquire Concept Relations[J]. Information Science, 2009, 27(7): 1085-1088.)
[25] 叶圣俊, 孙济庆, 李楠. 基于词素的中文术语语义关联研究[J]. 图书馆杂志, 2017, 36(1): 80-87.
[25] (Ye Shengjun, Sun Jiqing, Li Nan.Research on Semantic Relationship Correlation of Chinese Terminology Based on Morpheme Theory[J]. Library Journal, 2017, 36(1): 80-87.)
[26] Hearst M A.Automatic Acquisition of Hyponyms from Large Text Corpora[C]//Proceedings of the 14th International Conference on Computational Linguistics.1992: 539-545.
[27] 谷俊, 严明, 王昊. 基于改进关联规则的本体关系获取研究[J]. 情报理论与实践, 2011, 34(12): 121-125.
[27] (Gu Jun, Yan Ming, Wang Hao.Research on Ontology Relation Acquisition Based on Improved Association Rules[J]. Information Studies: Theory & Application, 2011, 34(12): 121-125. )
[28] Rada R, Mili H, Bicknell E, et al.Development and Application of a Metric on Semantic Nets[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1989, 19(1): 17-30.
[29] Richardson R, Smeaton A, Murphy J.Using WordNet as a Knowledge Base for Measuring Semantic Similarity Between Words[R]. Technical Report Working Paper CA-1294, School of Computer Applications, Dublin City University, 1994.
[30] Lord P W, Stevens R D, Brass A, et al.Investigating Semantic Similarity Measures Across the Gene Ontology: The Relationship Between Sequence and Annotation[J]. Bioinformatics, 2003, 19(10): 1275-1283.
[31] Resnik O.Semantic Similarity in a Taxonomy: An Information-Based Measure and Its Application to Problems of Ambiguity and Natural Language[J]. Journal of Artificial Intelligence Research, 1999(11): 95-130.
[32] Knappe R, Bulskov H, Andreasen T.On Similarity Measures for Content-based Querying[C]//Proceedings of the 10th International Fuzzy Systems Association World Congress. 2003: 400-403.
[33] 胡昌平, 陈果. 共词分析中的词语贡献度特征选择研究[J]. 现代图书情报技术, 2013(7): 89-93.
[33] (Hu Changping, Chen Guo.A New Feature Selection Method Based on Term Contribution in Co-word Analysis[J]. New Technology of Library and Information Service, 2013(7): 89-93.)
[34] 39疾病百科-心血管内科疾病[EB/OL]. [2016-10-10]. .
[34] (39 Wiki of Diseases-Cardiovascular Diseases [EB/OL]. [2016-10-10].
[35] 39疾病百科-高血压疾病知识[EB/OL]. [2016-10-10]. .
[35] (39 Wiki of Diseases-Hypertension [EB/OL]. [2016-10-10].
[36] NLPIR汉语分词系统[EB/OL]. [2016-05-10]. .
[36] (The NLPIR Chinese Word Segmentation System [EB/OL]. [2016-05-10].
[1] Jing Xie,Jingdong Wang,Zhenxin Wu,Zhixiong Zhang,Ying Wang,Zhifei Ye. Building Semantic Enrichment Framework for Scientific Literature Retrieval System[J]. 数据分析与知识发现, 2017, 1(4): 84-93.
[2] Ding Heng,Lu Wei. Building Standard Literature Knowledge Service System[J]. 现代图书情报技术, 2016, 32(7-8): 120-128.
[3] Wang Ying, Zhang Zhixiong, Li Chuanxi, Liu Yi, Tang Yijie, Zhou Zijian, Qian Li, Fu Honghu. The Design and Implementation of Open Engine System for Scientific & Technological Knowledge Organization Systems[J]. 现代图书情报技术, 2015, 31(10): 95-101.
[4] Liu Yi, Tang Yijie, Zhou Zijian, Yang Ru, Li Chuanxi, Zhang Xianfeng, Liu Chunjiang. Research and Construct of the Service Interface in STKOS Sharing Infrastructure[J]. 现代图书情报技术, 2014, 30(7): 9-16.
[5] Wang Chuanqing, Bi Qiang. System Model of Digital Library Automatic Semantic Annotation Tool[J]. 现代图书情报技术, 2014, 30(6): 17-24.
[6] Li Xiaoying, Li Danya, Qian Qing, Sun Haixia, Li Junlian, Hu Tiejun. Research on Automatic Algorithm of Finding English Synonymous Relations for Knowledge Organization System Integration[J]. 现代图书情报技术, 2014, 30(5): 26-32.
[7] Li Peng, Zhu Lijun, Liu Yajie, Yan Yingying. Realization of Improved RBAC Model in Task Management in Normative Concepts Collaborative Construction Platform[J]. 现代图书情报技术, 2014, 30(2): 86-91.
[8] Xu Xin, Hong Yunjia. Study on Text Visualization of Clustering Result for Domain Knowledge Base —— Take Knowledge Base of Chinese Cuisine Culture as the Object[J]. 现代图书情报技术, 2014, 30(10): 25-32.
[9] Zhao Yuxiang,Peng Xixian. Media as a Community? Literature Based Topic Evaluation in Information Systems Discipline[J]. 现代图书情报技术, 2014, 30(1): 56-65.
[10] Xie Jing, Qian Aibing, Han Pu, Su Xinning. Knowledge Organization Tool Catering to Service: Today and Future[J]. 现代图书情报技术, 2013, 29(9): 8-14.
[11] Zhang Yunliang, Zhang Zhaofeng, Zhang Xiaodan, Xu Deshan. Web Dynamic Interactive Visualization of Knowledge Organization Systems with D3.js[J]. 现代图书情报技术, 2013, 29(7/8): 127-131.
[12] Song Peiyan, Li Jingjing, Zhao Xing. Recommended Method for Cross-language Term Synonymous Relationship and Its Empirical Research[J]. 现代图书情报技术, 2013, (5): 40-45.
[13] Li Yazi, Sun Haixia, Jiang Jun, Qian Qing. Design and Implementation of Role Management in Collaborative System[J]. 现代图书情报技术, 2013, 29(2): 77-81.
[14] Zhang Pengyi, Qu Yan, Huang Chen. Design and Application of the S&T Innovation Group and Environment Ontology[J]. 现代图书情报技术, 2013, (12): 42-47.
[15] Li Yingying, Wang Huilin. Application of Topic Maps in Consumer Health Information Resources Organization——Illustrated by Diabetes Mellitus Information Resources[J]. 现代图书情报技术, 2013, (12): 55-61.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938