计算机科学 ›› 2019, Vol. 46 ›› Issue (2): 35-41.doi: 10.11896/j.issn.1002-137X.2019.02.006

• 大数据与数据科学 • 上一篇    下一篇

基于城市安全知识图谱的多关键词流式并行检索算法

管健, 汪璟玢, 卞倩虹   

  1. 福州大学数学与计算机科学学院 福州350116
  • 收稿日期:2018-07-13 出版日期:2019-02-25 发布日期:2019-02-25
  • 通讯作者: 汪璟玢(1973-),女,硕士,副教授,主要研究方向为海量数据管理、网络数据库和智能技术等,E-mail:wjbcc@263.net
  • 作者简介:管 健(1994-),男,硕士,主要研究方向为海量数据管理和智能技术等,E-mail:963016674@qq.com;卞倩虹(1993-),女,硕士,主要研究方向为海量数据管理和网络数据库等。
  • 基金资助:
    本文受国家自然科学青年基金资助项目(61300104),福建省自然科学基金项目(2017J01755)资助。

Multi-keyword Streaming Parallel Retrieval Algorithm Based on Urban Security Knowledge Graph

GUANJian, WANG Jing-bin, BIAN Qian-hong   

  1. College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350116,China
  • Received:2018-07-13 Online:2019-02-25 Published:2019-02-25

摘要: 我国智慧城市安全概念的普及和建设的逐渐落地,以及大数据在智慧城市安全建设方面的深度应用,对关键词检索的处理响应速度提出了更高的要求。针对这一问题,提出了基于城市安全知识图谱的流式知识图谱多关键词并行检索算法(MKPRASKG),该算法能够根据用户输入的查询关键字,通过关联类图的构建、剪枝和融合操作实时构建基于知识图谱实体的查询子图集,再结合评分函数,以高评分的查询子图为指引,在知识图谱实例数据中进行并行搜索,最终返回Top-k查询结果。实验结果证明,该算法在实时搜索、响应时间、搜索效果以及可扩展性等方面均具有较大的优势。

关键词: 多关键词检索, 流式, 实时, 知识图谱

Abstract: With the popularization and construction of the concept of smart city security in China,and the deep application of big data in the construction of smart city security,higher requirements on the processing response speed of keyword retrieval are needed.Aiming at this problem,this paper proposed a streaming multi-keyword parallel retrieval algorithm based on the urban security knowledge graph (MKPRASKG).This algorithm can construct a query subgraph set based on the entities of knowledge graph through the construction,pruning and fusion operation of the associated class graphs based on the query keywords input by the user in real time.And then combined with the scoring function,the high-scoring query subgraph is used as a guide,and the parallel search is performed in the knowledge graph instance data,and finally the Top-k query results are returned.Experimental results show that this algorithm has great advantages in terms of real-time search,response time,search effect and scalability.

Key words: Knowledge graph, Multi-keyword search, Real time, Streaming

中图分类号: 

  • TP319
[1]CHEN H S,HAN Z,DENG S N.Analysis and Research of Big Data Security in Smart Cities[J].Information Network Security,2015(7):1-6.(in Chinese)
陈红松,韩至,邓淑宁.智慧城市中大数据安全分析与研究[J].信息网络安全,2015(7):1-6.
[2]WAN S,LU J,FAN P,et al.To Smart City:Public Safety Network Design for Emergency[J].IEEE Access,2018,6(99):1451-1460.
[3]WANG Y Z,JIN X L,CHENG X Q.Network Big Data:Current Status and Prospects[J].Chinese Journal of Computers,2013,36(6):1125-1138.(in Chinese)
王元卓,靳小龙,程学旗.网络大数据:现状与展望[J].计算机学报,2013,36(6):1125-1138.
[4]MENG X F,CI X.Big Data Management:Concepts,Technolo- gies and Challenges [J].Journal of Computer Research and Development,2013,50(1):146-169.(in Chinese)
孟小峰,慈祥.大数据管理:概念、技术与挑战[J].计算机研究与发展,2013,50(1):146-169.
[5]BUXTON B,GOLDSTON D,DOCTOROW C,et al.Big data: science in the petabyte era[J].Nature,2008,455(7209):8-9.
[6]CHEN C.Streaming big data real-time processing technology,platform and application [J].Big Data,2017,3 (4):1-8.(in Chinese)
陈纯.流式大数据实时处理技术、平台及应用[J].大数据,2017,3(4):1-8.
[7]DUAN Z Y.Research on load balancing and fault tolerance mechanism of big data streaming system [D].Beijing:North China Electric Power University,2017.(in Chinese)
段泽源.大数据流式处理系统负载均衡与容错机制的研究[D].北京:华北电力大学,2017.
[8]HIRIYANNAIAH S,SIDDESH G M,SRINIVASA K G,et al.Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis[J].International Journal of Information Technology & Web Engineering,2018,13(3):99-111.
[9]BORTHAKUR D,GRAY J,SARMA J S,et al.Apache hadoop goes realtime at Facebook[C]∥ACM SIGMOD International Conference on Management of Data(SIGMOD 2011).Athens,Greece,DBLP,2011:1071-1080.
[10]KEETON K,PATTERSON D A,HE Y Q,et al.Performance characterization of a Quad Pentium Pro SMP using OLTP workloads:Technical Report UCB//CSD-98-1001[R].University of California at Berkeley,Computer Science Division,1998:15-26.
[11]CHEN H,MIGLIAVACCA M.StreamDB:A Unified Data Ma- nagement System for Service-Based Cloud Application[C]∥IEEE International Conference on Services Computing.IEEE Computer Society,2018:169-176.
[12]GUI H,FENG Y C,LI Y K.Research on data management system oriented to stream data[J].Journal of Computer Applications,2005,22(1):88-90.(in Chinese)
桂浩,冯玉才,李又奎.面向流数据的数据管理系统的研究[J].计算机应用研究,2005,22(1):88-90.
[13]CARNEY D,ETINTEMEL U,CHERNIACK M,et al.Monitoring streams:a new class of data management applications[C]∥Proc.International Conference on Very Large Data Bases.Hong Kong,China,2002:215-226.
[14]CHANDRASEKARAN S,COOPER O,DESHPANDE A,et al.TelegraphCQ:continuous dataflow processing[C]∥ACM SIGMOD International Conference on Management of Data.ACM,2003:668-668.
[15]HASSAN M,BANSAL S K.Semantic Data Querying over NoSQL Databases with Apache Spark[C]∥IEEE International Conference on Information Reuse and Integration.IEEE Computer Society,2018:364-371.
[16]HOU R J,FANG J,ZHANG J J.A data query method for real-time write protection of streaming data[J].Journal of Computer Applications,2014,31(9):2736-2740.(in Chinese)
侯荣军,房俊,张建静.一种流数据实时写入保障下的数据查询方法[J].计算机应用研究,2014,31(9):2736-2740.
[17]VIRGILIO R D,MACCIONI A.Distributed Keyword Search over RDF via MapReduce[C]∥European Semantic Web Confe-rence.Springer,Cham,2014:208-223.
[1] 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺.
时序知识图谱表示学习
Temporal Knowledge Graph Representation Learning
计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204
[2] 饶志双, 贾真, 张凡, 李天瑞.
基于Key-Value关联记忆网络的知识图谱问答方法
Key-Value Relational Memory Networks for Question Answering over Knowledge Graph
计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277
[3] 吴子仪, 李邵梅, 姜梦函, 张建朋.
基于自注意力模型的本体对齐方法
Ontology Alignment Method Based on Self-attention
计算机科学, 2022, 49(9): 215-220. https://doi.org/10.11896/jsjkx.210700190
[4] 孔世明, 冯永, 张嘉云.
融合知识图谱的多层次传承影响力计算与泛化研究
Multi-level Inheritance Influence Calculation and Generalization Based on Knowledge Graph
计算机科学, 2022, 49(9): 221-227. https://doi.org/10.11896/jsjkx.210700144
[5] 秦琪琦, 张月琴, 王润泽, 张泽华.
基于知识图谱的层次粒化推荐方法
Hierarchical Granulation Recommendation Method Based on Knowledge Graph
计算机科学, 2022, 49(8): 64-69. https://doi.org/10.11896/jsjkx.210600111
[6] 程成, 降爱莲.
基于多路径特征提取的实时语义分割方法
Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction
计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157
[7] 王杰, 李晓楠, 李冠宇.
基于自适应注意力机制的知识图谱补全算法
Adaptive Attention-based Knowledge Graph Completion
计算机科学, 2022, 49(7): 204-211. https://doi.org/10.11896/jsjkx.210400129
[8] 马瑞新, 李泽阳, 陈志奎, 赵亮.
知识图谱推理研究综述
Review of Reasoning on Knowledge Graph
计算机科学, 2022, 49(6A): 74-85. https://doi.org/10.11896/jsjkx.210100122
[9] 邓凯, 杨频, 李益洲, 杨星, 曾凡瑞, 张振毓.
一种可快速迁移的领域知识图谱构建方法
Fast and Transmissible Domain Knowledge Graph Construction Method
计算机科学, 2022, 49(6A): 100-108. https://doi.org/10.11896/jsjkx.210900018
[10] 杜晓明, 袁清波, 杨帆, 姚奕, 蒋祥.
军事指控保障领域命名实体识别语料库的构建
Construction of Named Entity Recognition Corpus in Field of Military Command and Control Support
计算机科学, 2022, 49(6A): 133-139. https://doi.org/10.11896/jsjkx.210400132
[11] 熊中敏, 舒贵文, 郭怀宇.
融合用户偏好的图神经网络推荐模型
Graph Neural Network Recommendation Model Integrating User Preferences
计算机科学, 2022, 49(6): 165-171. https://doi.org/10.11896/jsjkx.210400276
[12] 钟将, 尹红, 张剑.
基于学术知识图谱的辅助创新技术研究
Academic Knowledge Graph-based Research for Auxiliary Innovation Technology
计算机科学, 2022, 49(5): 194-199. https://doi.org/10.11896/jsjkx.210400195
[13] 徐涛, 陈奕仁, 吕宗磊.
基于改进YOLOv3的机坪工作人员反光背心检测研究
Study on Reflective Vest Detection for Apron Workers Based on Improved YOLOv3 Algorithm
计算机科学, 2022, 49(4): 239-246. https://doi.org/10.11896/jsjkx.210200119
[14] 朱敏, 梁朝晖, 姚林, 王翔坤, 曹梦琦.
学术引用信息可视化方法综述
Survey of Visualization Methods on Academic Citation Information
计算机科学, 2022, 49(4): 88-99. https://doi.org/10.11896/jsjkx.210300219
[15] 李嘉睿, 凌晓波, 李晨曦, 李子木, 杨家海, 张蕾, 吴程楠.
基于贝叶斯攻击图的动态网络安全分析
Dynamic Network Security Analysis Based on Bayesian Attack Graphs
计算机科学, 2022, 49(3): 62-69. https://doi.org/10.11896/jsjkx.210800107
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!