Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 1-10.

• Review •     Next Articles

Research Progress and Challenges on Association Graph

YIN Liang1,YUAN Fei2,3,XIE Wen-bo2,3,WANG Dong-zhi4,SUN Chong-jing2,3   

  1. Academy of Armored Force Engineering,Beijing 100072,China1
    Big Data Research Center,University of Electronic Science and Technology of China,Chengdu 611731,China2
    School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China3
    School of Computer Science and Technology,School of Computer Science and Technology,Mianyang,Sichuan 621010,China4
  • Online:2018-06-20 Published:2018-08-03

Abstract: With the development of web technology and projects such as Linked Open Data having been carried out,the association graph has made significant contributions on many areas such as Internet intelligent search,library bibliographic management,medicine and intelligent manufacturing.This paper reviewed the key topics of the association graph,including definition,framework and construction etc.The research progress on entity extraction,relationship extraction and knowledge fusion are discussed thoroughly.Furthermore,some challenges on association graph are also summarized.

Key words: Association graph, Entity extraction, Knowledge fusion, Relation extraction

CLC Number: 

  • TP391
[1]SINGHAL A.Official Google Blog:Introducing the Knowledge Graph:things,not strings.http://www.mendeley.com/catalog/official-google-blog-introducing-knowledge-graph-things-not-strings.
[2]BRACHMAN R J.What IS-A is and isn't:An analysis of taxonomic links in semantic networks.United States Journal of Computer,1983,16(10):30-36.
[3]STEINER T,VERBORGH R,TRONCY R,et al.Adding realtime coverage to the google knowledge graph[C]∥International Conference on Posters & Demonstrations Track-Volume 914.CEUR-WS.org,2012:65-68.
[4]WANG Z C,WANG Z G,LI J Z,et al.Knowledge extraction from Chinese wiki encyclopedias.Frontiers of Information Technology & Electronic Engineering,2012,13(4):268-280.
[5]ZENG Y,WANG H,HAO H,et al.Statistical and structural analysis of web-based collaborative knowledge bases generated from Wiki Encyclopedia[C]∥2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 01.IEEE Computer Society,2012:553-557.
[6]HUANG Z,CHUANG W,ONG T H,et al.A graph-based re- commender system for digital library[C]∥2nd ACM/IEEE-CS Joint Conference on Digital Libraries.ACM,2002:65-73.
[7]SIEK J G,LEE L Q,LUMSDAINE A.The Boost Graph Library: User Guide and Reference Manual,Portable Documents.Canada:Pearson Education,2001:17.
[8]DAI X,LI J,LIU T,et al.HRGRN:A Graph Search-Empowe- red Integrative Database of Arabidopsis Signaling Transduction,Metabolism and Gene Regulation Networks.Plant and Cell Physiology,2016,57(1):12.
[9]DIESTEL R,KR?L D,SEYMOUR P.Graph theory.Oberwolfach Reports,2016,13(1):51-86.
[10]WALKINGSHAW A D,ALEKANDROVSKY B L,VAN-HOFF A A,et al.Generating an Implied Object Graph Based on User Behavior:U.S.Patent Application 14/691,370.https://patents.google.com/patent/US20150227563.
[11]NARAYANAN S,NANDAGOPAL V,SUN E.Automatically generating nodes and edges in an integrated social graph:U.S.Patent 9,002,898.2015-4-7.
[12]李涓子.知识图谱:大数据语义链接的基石.http:// www.cipsc.org.cn/ kg2/.LI Juan-zi.Knowledge graph:the foundation for big data semantic link.(2015-02-20).http://www.cipsc.org.cn/kg2.
[13]刘峤,李杨,杨段宏,等.知识图谱构建技术综述.计算机研究与发展,2016,53(3):582-600.
[14]徐增林,盛泳潘,贺丽荣,王雅芳.知识图谱技术综述.电子科技大学学报,2016,45(4):589-606.
[15]耿霞,张继军,李蔚妍.知识图谱构建技术综述.计算机科学,2014,41(7):148-152.
[16]刘显敏,李建中.基于建规则的XML实体抽取方法.计算机研究与发展,2014,51(1):64-75.
[17]Wikimedia Foundation Inc.simple API for XML. .http:// en.wikipedia.org/ wiki/simpl_API_for_XML.
[18]黎玲利,高宏.基于距离度量的实体识别算法.智能计算机与应用,2014,4(6):61-63.
[19]刘雪莉,王宏志,等.基于实体的相似性连接算法.软件学报,2015,26(6):1421-1437.
[20]贾真,何大可,杨燕,等.基于弱监督学习的中文网络百科关系抽取.智能系统学报,2015,10(1):113-119.
[21]王俊华,左万利,闫昭.基于朴素贝叶斯模型的单词语义相似度度量.计算机研究与发展,2015,52(7):1499-1509.
[22]刘绍毓,周杰,李弼程,等.基于多分类 SVM-KNN 的实体关系抽取方法.数据采集与处理,2015,30(1):202-210.
[23]刘晓勇.一种基于树核函数的半监督关系抽取方法研究.山东大学学报(工学版),2015,45(2):22-26.
[24]MINTZ M,BILLS S,SNOW R,et al.Distant Supervision for Relation Extraction Without Labeled Data[C]∥Joint Confe-rence of the Meeting of the Acl & the International Joint Confe-rence on Natural Language Processing of the Afnlp:Volume.Association for Computational Linguistics,2009:1003-1011.
[25]CHENG A,XIA F,GAO J.A comparison of unsupervised me- thods for Part-of-Speech Tagging in Chinese[C]∥23rd International Conference on Computational Linguistics:Posters.Associa-tion for Computational Linguistics,2010:135-143.
[26]BANKO M,CAFARELLA M J,SODERLAND S,et al.Open Information Extraction from the Web[C]∥International Joint Conference on Artifical Intelligence.2007:2670-2676.
[27]ZHU J,NIE Z,LIU X,et al.StatSnowball:a statistical approach to extracting entity relationships[C]∥Proceedings of the 18th international conference on World wide web.ACM,2009:101-110.
[28]WU F,WELD D S.Open information extraction using Wikipedia∥Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics.Association for Computational Linguistics,2010:118-127.
[29]FADER A,SODERLAND S,ETZIONI O.Identifying relations for open information extraction[C]∥Proceedings of the Confe-rence on Empirical Methods in Natural Language Processing.Association for Computational Linguistics,2011:1535-1545.
[30]ETZIONI O,CAFARELLA M ,BANKO M.Open information extraction.https://doi.org/10.1142/S2425038416300032 [31]BATISTA D S,MARTINS B,SILVA M J.Semi-supervised boot- strapping of relationship extractors with distributional semantics[C]∥2015 Conference on Empirical Methods in Natural Language Processing.Association for Computational Linguistics,Lisbon,Portugal,2015:499-504.
[32]BRIN S.Extracting patterns and relations from world wide web[C]∥WebDB Workshop at 6th International Conference on Extending Database Technology (WebDB’98).1998:172-183.
[33]AGICHTEIN E,GRAVANO L.Snowball:Extracting relations from large plain-text collections[C]∥fifth ACM conference on Digital libraries.ACM,2000:85-94.
[34]罗甫林,黄鸿,刘嘉敏,等.基于半监督稀疏流形嵌入的高光谱影像特征提取.电子与信息学报,2016,38(9):2321-2329.
[35]MADAAN A,MITTAL A,RAMAKRISHNAN G,et al.Numerical relation extraction with minimal supervision[C]∥Thirtieth AAAI Conference on Artificial Intelligence.2016.
[36]NICKEL M,MURPHY K,TRESP V,et al.A Review of Relational Machine Learning for Knowledge Graphs.Proceedings of the IEEE,2015,104(1):11-33.
[37]黄卫春,徐力,熊李艳,等.基于信息增益的 Web 人物关系抽取.计算机应用研究,2016,33(8):2286-2289.
[38]HASEGAWA T,SEKINE S,GRISHMAN R.Discovering relations among named entities from large corpora[C]∥Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics.Association for Computational Linguistics,2004:415.
[39]ZHU J,NIE Z,LIU X,et al.StatSnowball:a statistical approach to extracting entity relationships[C]∥18th International Conference on World Wide Web.ACM,2009:101-110.
[40]秦兵,刘安安,刘挺.无指导的中文开放式实体关系抽取.计算机研究与发展,2015,52(5):1029-1035.
[41]AGGARWAL N,BUITELAAR P.Wikipedia-based distribu- tional semantics for entity relatedness[C]∥2014 AAAI Fall Symposium Series.2014.
[42]ZOBEL J,MOFFAT A.Exploring the similarity space.Acm Sigir Forum,1998,32(1):18-34.
[43]TOUTANOVA K,CHEN D,PANTEL P,et al.Representing Text for Joint Embedding of Text and Knowledge Bases[C]∥EMNLP.2015:1499-1509.
[44]刘明辉,王磊,党林阁,等.非确定先验信息的贝叶斯网结构学习方法.计算机工程,2010,36(5):165-167.
[45]SHAW C A,CAMPBELL I M.Variant interpretation through Bayesian fusion of frequency and genomic knowledge.Genome medicine,2015,7(1):4.
[46]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.Decision Support Systems,2015,79(C):24-32.
[47]张振海,王晓明,党建武,等.基于专家知识融合的贝叶斯网络结构学习方法.计算机工程与应用,2014,50(2):1-4.
[48]MARCHEGGIANI D,TITOV I.Discrete-state variational auto encoders for joint discovery and factorization of relations.Transactions of the Association for Computational Linguistics,2016(4):231-244.
[49]韩立岩,周芳.基于 DS 证据理论的知识融合及其应用.北京航空航天大学学报,2006,32(1):65-68.
[50]宋亚飞,王晓丹,雷蕾 .基于直觉模糊集的时域证据组合方法研究.自动化学报,2016,42(9):1322-1338.
[51]SHAFER G.A mathermatical theory of evidence .Princeton,NJ:Princeton University Press,1976.
[52]郭强,关欣,潘丽娜,等.一种基于条件证据网络的多源异类知识融合识别方法.控制与决策,2015,30(12):2153-2160.
[53]屈强,刘中晅,陈波.基于修正倒数型距离贴近度的传感器数据模糊加权融合法.计算机工程,2016,42(5):313-316.
[54]XIE N,WANG W,MA B.et al.Research on an Agricultural Knowledge Fusion Method for Big Data.https://www.researchgate.net/publication/277962505_Research_on_an_Agricultural_Knowledge_Fusion_Method_for_Big_Data.
[55]陈云翔,蔡忠义,张诤敏,等.基于证据理论和直觉模糊集的群决策信息集结方法.系统工程与电子技术,2015,37(3):594-598.
[56]Wikipedia.Knowledge graph..https://en.Wikipedia.org/wiki/ Knowledge _Graph.
[57]HASNAIN A,DUNNE N,DECKER S.Knowledge Processing with Big Data and Semantic Web Technologies.2015.
[58]WALKINGSHAW A D,ALEKSANDROVSKY B L,VAN-HOFF A A,et al.Generating an Implied Object Graph Based on User Behavior:U.S.Patent Application 14/691,370.2015-4-20.
[59]LI Y,MARTINEZ O,CHEN X,et al.In a World That Counts:Clustering and Detecting Fake Social Engagement at Scale[C]∥25th International Conference on World Wide Web.Internatio-nal World Wide Web Conferences Steering Committee,2016:111-120.
[60]SHADBOLT N,BERNERS-LEE T,HALL W.The semantic web revisited.IEEE Intelligent Systems,2006,21(3):96-101.
[61]BERNERS-LEE T,CHEN Y,CHILTON L,et al.Tabulator:Exploring and Analyzing linked data on the Semantic Web[C]∥Proceedings of the 3rdInternational Semantic Web User Interac-tion Workshop.2006.
[62]HANNEMANN J,KETT J.Linked Data and Libraries. .http://www.ifla.org/ files/hq/papers/ ina76/149-hannemann-en.pdf [63]MALMSTEN M,李雯静.将图书馆目录纳入语义万维网.数据分析与知识发现,2009,3(3):3-7.
[64]SUMMERS,ANTOINE,ISAAC,等.LCSH,SKOS和关联数据.数据分析与知识发现,2009,3(3):8-14.
[65]SCHMACHTENBERG M ,BIZER C .Linking Open Data cloud diagram.http://lod-cloud.net/.
[66]WANG C,MARSHALL A,ZHANG D,et al.ANAP:an integrated knowledge base for Arabidopsis protein interaction network analysis.Plant physiology,2012,158(4):1523-1533.
[67]BRANDO M M,DANTAS L L,SILVA FILHO M C.AtPIN:Arabidopsis thaliana protein interaction network..Bmc Bioinformatics,2009,10(1):1-7.
[68]SAIER JR M H,REDDY V S,TAMANG D G,et al.The transporter classification database.Nucleic acids research,2013,42(1):251-258.
[69]DAI X,ZHAO P X.psRNATarget:a plant small RNA target analysis server.Nucleic Acids Research,2011(39):155-159.
[70]BARRETT T,WILHITE S E,LEDOUX P,et al.NCBI GEO:archive for functional genomics data sets-update.Nucleic acids research,2012,41(1):991-995.
[1] LU Liang, KONG Fang. Dialogue-based Entity Relation Extraction with Knowledge [J]. Computer Science, 2022, 49(5): 200-205.
[2] XU Jin. Construction and Application of Knowledge Graph for Industrial Assembly [J]. Computer Science, 2021, 48(6A): 285-288.
[3] LYU Jin-na, XING Chun-yu , LI Li. Video Character Relation Extraction Based on Multi-feature Fusion and Fine-granularity Analysis [J]. Computer Science, 2021, 48(4): 117-122.
[4] HANG Ting-ting, FENG Jun, LU Jia-min. Knowledge Graph Construction Techniques:Taxonomy,Survey and Future Directions [J]. Computer Science, 2021, 48(2): 175-189.
[5] HOU Tong-jia, ZHOU Liang. Chinese Ship Fault Relation Extraction Method Based on Bidirectional GRU Neural Network and Attention Mechanism [J]. Computer Science, 2021, 48(11A): 154-158.
[6] ZHANG Shi-hao, DU Sheng-dong, JIA Zhen, LI Tian-rui. Medical Entity Relation Extraction Based on Deep Neural Network and Self-attention Mechanism [J]. Computer Science, 2021, 48(10): 77-84.
[7] YU Yi-lin, TIAN Hong-tao, GAO Jian-wei and WAN Huai-yu. Relation Extraction Method Combining Encyclopedia Knowledge and Sentence Semantic Features [J]. Computer Science, 2020, 47(6A): 40-44.
[8] QIAN Xiao-mei,LIU Jia-yong,CHENG Peng-sen. Distant Supervised Relation Extraction Based on Densely Connected Convolutional Networks [J]. Computer Science, 2020, 47(2): 157-162.
[9] CHEN Xiao-jun, XIANG Yang. Construction and Application of Enterprise Risk Knowledge Graph [J]. Computer Science, 2020, 47(11): 237-243.
[10] MA Jian-hong, LI Zhen-zhen, ZHU Huai-zhong, WEI Zi-mo. Entity and Relationship Joint Extraction Method of Feedback Mechanism [J]. Computer Science, 2019, 46(12): 242-249.
[11] LI Hao, LIU Yong-jian, XIE Qing, TANG Ling-li. Distant Supervision Relation Extraction Model Based on Multi-level Attention Mechanism [J]. Computer Science, 2019, 46(10): 252-257.
[12] MA Xiao-jun, GUO Jian-yi, XIAN Yan-tuan, MAO Cun-li, YAN Xin and YU Zheng-tao. Entity Hyponymy Acquisition and Organization Combining Word Embedding and Bootstrapping in Special Domain [J]. Computer Science, 2018, 45(1): 67-72.
[13] LI Ying, HAO Xiao-yan and WANG Yong. N-ary Chinese Open Entity-relation Extraction [J]. Computer Science, 2017, 44(Z6): 80-83.
[14] LV Zhao-jin, SHEN Li-wei and ZHAO Wen-yun. Scenario-oriented Location Method of Android Applications [J]. Computer Science, 2017, 44(2): 216-221.
[15] LIU Kai, FU Hai-dong, ZOU Yu-wei and GU Jin-guang. Chinese Medical Weak Supervised Relation Extraction Based on Convolution Neural Network [J]. Computer Science, 2017, 44(10): 249-253.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!