[1] WALTZE E,LINAS J. Intelligence fusion pushed[J]. Aviation week and space technology,1979(1):205-211.
[2] TAHANI H,KELLER J M. Information fusion in computer vision using the fuzzy integral[J].IEEE transactions on systems man & cybernetics, 1990, 20(3):733-741.
[3] 刘晓娟,李广建,化柏林.知识融合:概念辨析与界说[J].图书情报工作,2016,60(13):13-19,32.
[4] 祝振媛,李广建."数据-信息-知识"整体视角下的知识融合初探——数据融合、信息融合、知识融合的关联与比较[J].情报理论与实践,2017,40(2):12-18.
[5] SMIRNOV A, PASHKIN M, CHILOV N, et al. Multi-agent architecture for knowledge fusion from distributed sources[C]//International workshop of central and eastern Europe on multi-agent systems. Heidelberg:Springer, 2001:293-302.
[6] MARTENS D, DE BACKER M, HAESEN R, et al. Ant-based approach to the knowledge fusion problem[C]//International workshop on ant colony optimization and swarm intelligence. Heidelberg:Springer, 2006:84-95.
[7] HU X, HU J, SEKHARI A, et al. A fuzzy knowledge fusion framework for terms conflict resolution in concurrent engineering[J]. Concurrent engineering, 2011,19(1):71-84.
[8] 邱均平,余厚强.知识科学视角下国际知识融合研究进展与趋势[J].图书情报工作,2015, 59(8):126-132,148.
[9] REECE A,HUI K,GRAY A,et al. Designing for scalability in a knowledge fusion problem[C]//International workshop on ant colony optimization and swarm intelligence. Heidelberg:Springer, 2006:84-95.
[10] HU X, HU J, SEKHARI A, et al. A fuzzy knowledge fusion framework for terms conflict resolution in concurrent engineering[J]. Concurrent engineering, 2011,19(1):71-84.
[11] FONTANI M, ARGONES-RUA E, TRONCOSO C, et al. The watchful forensic analyst:multi-clue information fusion with background knowledge[C]//IEEE International Workshop on Information Forensics and Security. Guangzhou:IEEE, 2013:120-125.
[12] FISCH D, KALKOWSKI E, SICK B. Knowledge fusion for probabilistic generative classifiers with data mining applications[J]. IEEE transactions on knowledge and data engineering, 2014, 26(3):652-666.
[13] SMIRNOV A, LEVASHOVA T, SHILOV N. Patterns for context-based knowledge fusion in decision support systems[J]. Information fusion, 2015, 21:114-129.
[14] 高劲松, 梁艳琪. 关联数据环境下知识融合模型研究[J]. 情报科学, 2016, 34(2):50-54.
[15] TAI C H, CHANG C T, CHANG Y S. Hybrid knowledge fusion and inference on cloud environment[J]. Future generation computer systems, 2018, 87:568-579.
[16] KRIEGEL E U, PFENNIGSCHMIDT S, ZIEGLER H G. Practical aspects of the use of a knowledge fusion toolkit in safety applications[C]//IEEE eleventh international symposium on autonomous decentralized systems. Mexico:IEEE, 2013:1-4.
[17] 沈旺, 李亚峰, 侯昊辰. 数字参考咨询知识融合框架研究[J]. 图书情报工作, 2013,57(19):139-143.
[18] LIU J, XU W, ZHAN H. Multi-source and heterogeneous knowledge organization and representation for knowledge fusion in cloud manufacturing[C]//Proceedings of international conference on soft computing techniques and engineering application. New Delhi:Springer, 2014:55-61.
[19] KAWTRAKUL A, KHUNTHONG V, SUKTARACHAN M, et al. Development of an information integration and knowledge fusion platform for spatial and time based advisory services:precision farming as a case study[C]//Annual SRⅡ global conference. San Jose:IEEE, 2014:241-248.
[20] YUE K, QIAN W, FU X, et al. Qualitative probabilistic network-based fusion of time-series uncertain knowledge[J]. Soft computing, 2015, 19(7):1953-1972.
[21] LI F, DONG X L, LANGEN A, et al. Knowledge verification for long-tail verticals[J]. Proceedings of the VLDB endowment, 2017, 10(11):1370-1381.
[22] WANG F, FAN H, LIU G. Big data knowledge service framework based on knowledge fusion[C]//Proceedings of the international joint conference on knowledge discovery, knowledge engineering and knowledge management. Porto:Lda, 2016:116-123.
[23] 李立睿,邓仲华."互联网+"背景下科研用户的小数据融合研究[J].图书情报工作,2016, 60(6):58-63.
[24] 朱娟,唐晓波.基于三层知识融合模型的个性化商品推荐[J].图书馆学研究,2017(5):24-30.
[25] 黄新平. 政府网站信息资源多维语义知识融合研究[D].长春:吉林大学,2017.
[26] 周芳, 刘玉战, 韩立岩. 基于模糊集理论的知识融合方法研究[J]. 北京理工大学学报(社会科学版), 2013, 15(3):67-73.
[27] 谢能付.基于农业本体和融合规则的知识融合框架研究[J].安徽农业科学,2013,41(1):395-397.
[28] 陈为东, 王萍, 王益成, 等. 政府网站信息资源的多维语义知识融合结构体系及策略研究[J]. 情报理论与实践, 2017, 40(6):111-116.
[29] 周利琴,范昊,潘建鹏.网络大数据中的知识融合框架研究[J].情报杂志,2018,37(1):145-150,197.
[30] 王曰芬, 岑咏华. 大数据时代知识融合体系架构设计研究[J]. 数字图书馆论坛, 2016(10):16-24.
[31] 唐晓波, 朱娟, 杨丰华. 大数据环境下的知识融合框架模型研究[J]. 图书馆学研究, 2016(1):32-35.
[32] 张心源, 邱均平. 大数据环境下的知识融合框架研究[J]. 图书馆学研究, 2016(8):66-70.
[33] 苏新宁.面向知识服务的知识组织理论与方法[M].北京:科学出版社, 2014.
[34] TAN L, TANG D, WANG Q, et al. Open design pattern, method, and its self-organization mechanism[J]. Procedia CIRP, 2016, 56:34-39.
[35] 刘渭滨, 邹智元, 邢薇薇. 模式分类中的特征融合方法[J]. 北京邮电大学学报, 2017, 40(4):1-8.
[36] 朱娟,唐晓波.基于三层知识融合模型的个性化商品推荐[J].图书馆学研究,2017(5):24-30.
[37] 索传军,盖双双.知识元的内涵、结构与描述模型研究[J].中国图书馆学报, 2018, 44(4):54-72.
[38] 温有奎,焦玉英.基于知识元的知识发现[M].西安:西安电子科技大学出版社,2011.
[39] 缑锦. 知识融合中若干关键技术研究[D]. 杭州:浙江大学, 2005.
[40] SUN L. Knowledge element-based competitive intelligence analytics serving for SWOT situation assessment[C]//The 8th international symposium on computational intelligence and design. Hangzhou:IEEE, 2015, 1:576-579.
[41] 唐晓波,朱娟.大数据环境下知识融合的关键问题研究综述[J].图书馆杂志,2017,36(7):10-16.
[42] 唐晓波,魏巍.知识融合:大数据时代知识服务的增长点[J].图书馆学研究,2015(5):9-14,8.
[43] 孙琳,王延章.基于知识元的多源竞争情报融合方法研究[J].情报杂志,2017,36(11):65-71.
[44] WU W, LI H, WANG H, et al. Probase:a probabilistic taxonomy for text understanding[C]//Proceedings of the 2012 ACM SIGMOD international conference on management of data. Scottsdale:ACM, 2012:481-492.
[45] DONG X, GABRILOVICH E, HEITZ G, et al. Knowledge vault:a Web-scale approach to probabilistic knowledge fusion[C]//Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. New York:ACM, 2014:601-610.
[46] 林海伦, 王元卓, 贾岩涛, 等. 面向网络大数据的知识融合方法综述[J]. 计算机学报, 2017, 40(1):1-27.
[47] 田鹏伟, 张娴, 胡正银, 等. 异构信息网络融合方法研究综述[J]. 图书情报工作, 2017, 61(7):137-144.
[48] GOU J, JIANG Y, WU Y, et al. A new knowledge fusion method based on semantic rules[C]//The 8th international conference on Signal processing. Beijing:IEEE, 2006:1865-1868.
[49] FENG Z, LI-JUAN Z, XIAO-MING F, et al. The research of Vietnamese language news clue extraction method based on converged network semantic knowledge[C]//The 27th Chinese conference on control and decision. Qingdao:IEEE, 2015:3287-3289.
[50] WANG G, HU Y, TIAN X, et al. An integrated open approach to capturing systematic knowledge for manufacturing process innovation based on collective intelligence[J]. Applied sciences, 2018, 8(3):340.
[51] FENSEL D, DECKER S, ERDMANN M, et al. Ontobroker:how to make the WWW intelligent[C]//Proceedings of the 11th Banff knowledge acquisition for knowledge-based systems. Banff:AAAI, 1998:36-42.
[52] XIE N, WANG W, YANG X, et al. Rule-based agricultural knowledge fusion in Web information integration[J]. Sensor letters, 2012, 10(1/2):635-638.
[53] LIU J, XU W, JIANG H. Research on dynamic ontology construction method for knowledge fusion in group corporation[J]. Advances in intelligent systems & computing, 2014, 278:289-298.
[54] SMIRNOV A, KASHEVNIK A, MIKHAILOV S, et al. Multi-level robots self-organization in smart space:approach and case study[C]//Conference on smart spaces. Cham:Springer, 2015:68-79.
[55] FAN H, WANG F, ZHENG M. Research on knowledge fusion connotation and process model[C]//China conference on knowledge graph and semantic computing. Singapore:Springer, 2016:184-195.
[56] SANTOS JR E, WILKINSON J T, SANTOS E E. Fusing multiple bayesian knowledge sources[J]. International journal of approximate reasoning, 2011, 52(7):935-947.
[57] COUSSEMENT K, BENOIT D F, ANTIOCO M. A bayesian approach for incorporating expert opinions into decision support systems:a case study of online consumer-satisfaction detection[J]. Decision support systems, 2015, 79:24-32.
[58] ZHANG L, QIAN W, ZHANG Z, et al. Research on knowledge demand information acquisition for product design[C]//The 12th international conference on computational intelligence and security. Wuxi:IEEE,2016:677-680.
[59] YUE W, CHEN X, GUI W, et al. A knowledge reasoning fuzzy-bayesian network for root cause analysis of abnormal aluminum electrolysis cell condition[J]. Frontiers of chemical science and engineering, 2017, 11(3):414-428.
[60] DEMPSTER A P. Upper and lower probabilities induced by a multivalued mapping[J]. Annals of mathematical statistics, 1967, 38(2):325-339.
[61] YAN R, LI G, LIU B. Knowledge fusion based on DS theory and its application on expert system for software fault diagnosis[C]//Prognostics and system health management conference. Beijing:IEEE,2015:1-5.
[62] PENG G, MAO H, WANG H, et al. BOM-based design knowledge representation and reasoning for collaborative product development[J]. Journal of systems science and systems engineering, 2016, 25(2):159-176.
[63] SUN L, WANG Y. A multi-attribute fusion approach extending dempster-shafer theory for combinatorial-type evidences[J]. Expert systems with applications, 2018, 96:218-229.
[64] SUN L, WARD M P, LI R, et al. Global spatial risk pattern of highly pathogenic avian influenza H5N1 virus in wild birds:a knowledge-fusion based approach[J]. Preventive veterinary medicine, 2018, 152:32-39.
[65] LAO N, MITCHELL T, COHEN W W. Random walk inference and learning in a large scale knowledge base[C]//Proceedings of the conference on empirical methods in natural language processing. Stroudsburg:Association for Computational Linguistics,2011:529-539.
[66] WU Y, LEHMAN A, DUNAWAY D J. Evaluations of a large topic map as a knowledge organization tool for supporting self-regulated learning[J]. Knowledge organization, 2015, 42(6):386-398.
[67] LEVCHUK G, BLASCH E. Probabilistic graphical models for multi-source fusion from text sources[C]//Symposium on computational intelligence for security and defense applications. Verona:IEEE, 2015:1-10.
[68] KOUMOUTSOS G, FASLI M, LEWIN I, et al. Graph-based information exploration over structured and unstructured data[C]//IEEE international conference on big data. Boston:IEEE, 2017:1991-2000.