[Purpose/significance] There are many knowledge units in the network community, among which there are intricate relationships. It is necessary to carry out multiple knowledge relations mining uniformly and succinctly on the premise of retaining all the relations of knowledge units.[Method/process] This paper puts forward the solution of multi-knowledge relations mining in network community. Firstly, 3 typical knowledge units (users, texts and words) in the network community and their multiple relations in the knowledge communication were extracted into a supernetwork. Secondly, the network representation learning algorithm was used to uniformly represent the nodes in the supernetwork as low-dimensional dense vectors. Finally, multiple knowledge relations calculation was carried out based on nodal vector.[Result/conclusion] The effectiveness of the scheme was verified by taking cardiovascular BBS in dingxiang garden as an example. This scheme not only retains all the information of the knowledge unit, but also carries out the mining of the knowledge relation under the unified low-dimensional characteristics, and finally the knowledge relation meets the requirements of the diversity of the knowledge organization scene in the network community.
[1] 陈果,朱茜凌,肖璐.面向网络社区的知识聚合:发展、研究基础与展望[J].情报杂志,2017,36(12):193-197,192.
[2] 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.
[3] 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.
[4] 宁亚辉,樊兴华,吴渝.基于领域词语本体的短文本分类[J].计算机科学,2009,36(3):142-145.
[5] SAHAMI M,HEILMAN T D. Aweb-based kernel function for measuring thesimilarity of short textsnippets[C]//Proceedings of the 15thinternational conference on World Wide Web. New York:ACM, 2006:377-386.
[6] ZELIKOVITZ S,KOGAN M. Using web searches on important words to create background sets for LSI classification[C]//Proceedings of the 19th international FLAIRS conference. Florida:AAAI Press,2006:598-603.
[7] 黄微,高俊峰,李瑞,等. Folksonomy中Tag语义距离测度与可视化研究[J]. 现代图书情报技术,2014(7/8):64-70.
[8] TSUI E, WANG W M, CHEUNG C F, et al. A concept-relationship acquisition and inference approach for hierarchical taxonomy construction from tags[J]. Information processing &management,2010,46(1):44-57.
[9] 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.
[10] 石伟杰,徐雅斌.微博用户兴趣发现研究[J].现代图书情报技术,2015(1):52-58.
[11] 肖璐.基于知识超网络的网络社区学术资源多粒度聚合研究[J].情报杂志,2018,37(12):182-187,194.
[12] 王传清,毕强.超网络视域下的数字资源深度聚合研究[J].情报学报,2015,34(1):4-13.
[13] 张金柱,于文倩,刘菁婕,等.基于网络表示学习的科研合作预测研究[J].情报学报,2018,37(2):132-139.
[14] 林原,刘海峰,王海龙,等.基于表示学习的学者间潜在合作机会挖掘[J].情报杂志,2019,38(5):65-70.
[15] 樊玮,韩佳宁,张宇翔.基于网络表示学习的论文影响力预测算法[J].计算机工程,2019,45(12):160-165,170.
[16] 张潇鲲,刘琰,陈静.引入外部词向量的文本信息网络表示学习[J].智能系统学报,2019,14(5):1056-1063.
[17] 朱国进,李承前.网络知识资源表示学习模型[J].智能计算机与应用,2016,6(3):5-10.
[18] 朱靖雯,杨玉基,许斌,等.基于HowNet的语义表示学习[J].中文信息学报,2019,33(3):33-41.
[19] 韩忠明,郑晨烨,段大高,等.基于多信息融合表示学习的关联用户挖掘算法[J].计算机科学,2019,46(4):77-82.
[20] 杨奕卓,于洪涛,黄瑞阳,等.基于融合表示学习的跨社交网络用户身份匹配[J].计算机工程,2018,44(9):45-51.
[21] 尹赢,吉立新,黄瑞阳,等.网络表示学习的研究与发展[J].网络与信息安全学报,2019,5(2):77-87.
[22] 涂存超,杨成,刘知远,等.网络表示学习综述[J].中国科学:信息科学,2017,47(8):980-996.
[23] NAGURNEY A,DONG J. Supernetworks:decision-making for the information age[M]. Cheltenham:Edward Elgar Publishers,2002.
[24] 漆玉虎,郭进利.超网络研究[J].上海理工大学学报,2013,35(3):227-239.
[25] 周慧,赵中英,李超.面向异质信息网络的表示学习方法研究综述[J].计算机科学与探索,2019,13(7):1081-1093.
[26] 丁香园论坛.心血管专业讨论版[EB/OL].[2019-06-08]. http://cardiovascular.dxy.cn/bbs/board/47.
[27] 39疾病百科.心血管内科[EB/OL].[2019-06-08]. http://jbk.39.net/bw/xinxueguanneike.
[28] 张良富,李翠平,陈红. 大规模图上的SimRank计算研究综述[J]. 计算机学报,2019,42(12):2665-2682.
[29] 邱少明,於涛,杜秀丽,等.基于节点多属性相似凝聚的社团划分算法[J/OL].[2019-10-13]. http://kns.cnki.net/kcms/detail/31.1289.tp.20190808.1701.023.html.
[30] 百度百科.糖尿病高血压[EB/OL].[2019-10-13]. https://baike.baidu.com/item/%E7%B3%96%E5%B0%BF%E7%97%85%E9%AB%98%E8%A1%80%E5%8E%8B/1424758?fr=aladdin.
[31] 张菀桐, 胡元会, 朱宝琛,等. 冠心病合并高血压患者血压水平与血栓形成动力学相关性研究[J]. 现代中西医结合杂志, 2016, 25(2):129-131,140.
[32] 张鑫, 李荣, 黄玉晓,等. OSAHS对冠心病合并高血压患者动态血压及心率变异性的影响[J]. 中西医结合心脑血管病杂志, 2014, 12(1):35-37.
[33] YANG C,LIU Z Y,ZHAO D, et al. Network representation learning with rich text information[C]//Proceedings of the 24th international joint conference on arti? cial intelligence. Buenos Aires:AAAI Press, 2015:2111-2117.
[34] TU C C,LIU H,LIU Z Y,et al. CANE:context-aware network embedding for relation modeling[C]//Proceedings ofthe 55th annual meeting of the association for computational linguistics.Vancouver:ACL, 2017:1722-1731.
