基于微本体和图匹配的微博信息匹配算法研究

详细信息    查看全文 | 推荐本文 |

英文篇名：Research on Microblog Information Matching Algorithm Based on Micro-Ontology and Graph Matching 作者：崔金栋 ; 高志豪 英文作者：CUI Jin-dong;GAO Zhi-hao;School of Economic and Management, Northeast Electric Power University; 关键词：微博信息 ; 微本体 ; 图匹配 ; 信息推荐 英文关键词：microblog information;;micro-ontology;;graph matching;;information recommendation 中文刊名：QBKX 英文刊名：Information Science 机构：东北电力大学经济管理学院; 出版日期：2019-07-29 出版单位：情报科学 年：2019 期：v.37;No.336 基金：国家社科基金规划项目“基于信息生态的微博信息管理机理研究”(16BTQ068) 语种：中文; 页：QBKX201908007 页数：7 CN：08 ISSN：22-1264/G2 分类号：46-52

摘要

【目的/意义】随着广大微博用户对微博信息推荐要求的提高,如何提升微博信息推荐的精准度,实现内容的个性化推荐成为了研究者们亟待解决的热点问题。【方法/过程】笔者利用本体构建技术构建微博信息微本体,提出了一种基于微本体架构的微博信息内容推荐方法,使用SJ-Tree将微本体图匹配算法进行改良,完善主题微本体和用户微博信息微本体的匹配,实现微博信息的推荐。【结果/结论】仿真实验结果证实了微本体图匹配算法可以提高微博信息推荐的效率及精准度,为实现微博信息内容的定制化推荐打下了坚实的基础。
        【Purpose/significance】With the improvement of microblog information recommendation requirements by microblog users, how to improve the accuracy of microblog information recommendation and realize personalized recommendation of microblog information content has become an urgent problem for researchers.【Method/process】The author builds microblog information micro-ontology by using ontology construction technology, and proposes a micro-propagation information content recommendation method based on micro-ontology architecture. The SJ-Tree is used to improve the micro-ontology matching algorithm to improve the theme micro-ontology and user microblog information. The matching of the micro-ontology realizes the recommendation of the microblog information.【Result/conclusion】The simulation results confirm that the micro-ontology matching algorithm can effectively improve the efficiency and accuracy of microblog information recommendation, and lays a solid foundation for the personalized recommendation of microblog information content.

引文

【1 】Blei D M, Ng A Y, Jordan M I.Latent Dirichlet Allocation[J].The Journal of Machine Learning Research,2003,(3):993-1022.
    2 Tang Xuning, Yang C C.TUT:A Statistical Model for De-tecting Trends, Topics and User Interests in Social Media[C]//Proc.of the 21st ACM International Conference on Information and Knowledge Management.New York,USA:ACM Press, 2012:972-981.
    3 唐晓波,房小可.基于文本聚类与LDA相融合的微博主题检索模型研究[J].情报理论与实践,2013,36(08):85-90.
    4 徐志明,李栋,刘挺,李生,王刚,袁树仑.微博用户的相似性度量及其应用[J].计算机学报,2014,37(1):207-218.
    5 胡潜,明均仁.基于用户-主题关联挖掘的虚拟社区推荐方法研究[J].情报杂志,2017,36(6):156-159,185.
    6 石豪,李红娟,赖雯,赵英.基于folksonomy标签的用户分类研究[J].图书情报工作,2011,55(2):117-120.
    7 Hannon J,BennettM,SmythB.RecommendingTwitterUsers to Follow Using Content and Collaborative Filtering Approaches[C]//Proceedings of the 4th ACMConference on pecommender Systems(RecSys’2010).New York,USA,2010:99-206.
    8 刘紫玉,杨雨佳,张晓明,瞿英.基于DBpedia的领域本体进化方法研究[J].情报杂志, 2017, 36(06):160-166.
    9 刘紫玉,黄磊,杨明欣,冯兰萍.基于模块化本体的协同进化方法研究[J].情报杂志, 2013, 32(10):131-135.
    10 Shvaiko P, Euzenat J.Ontology matching:state of the art and future challenges[J].Knowledge and Data Engineering,IEEE Transactions on,2013, 25(1):158-176.
    11 Melnik S, Garcia-Molina H, Rahm E.Similarity flooding:A versatile graph matching algorithm and its application to schema matching[C]//Data Engineering, 2002.Proceedings.18th International Conference on.IEEE, 2002:117-128.
    12 Giunchiglia F, Shvaiko P, Yatskevich M.S-Match:an algorithm and an implementation of semantic matching[M].Berlin:Springer Berlin Heidelberg,2004.
    13 A.Rodriguez, M.Egenhofer.Determining Semantic Similarity Entity Classes from Different Ontologies[J].IEEE Transactionson Knowledge and Data Engineering, 2003, 15(2):442-456.
    14 崔金栋,杜文强,关杨,罗文达.微博用户信息个性化推荐主题模型LDA演化分析研究[J].情报科学,2017,35(8):3-10.
    15 崔金栋,徐宝祥,王新媛.基于微本体构建的微博信息管理机理研究[J].情报资料工作,2013,(5):50-54.
    16 崔金栋,孙遥遥,王欣,于圆美,王新媛.基于Folksonmy和本体融合的微博信息推荐方法研究[J].情报科学,2015,33(10):27-31.
    17 陈琨,张蕾.基于知识图的领域本体构建方法[J].计算机应用,2011,31(6):1664-1666,1670.
    18 欧阳丹彤,张瑜,叶育鑫.本体推理机求解Mups的性能评测研究[J].计算机学报,2017,40(6):1422-1439.