摘要
文本挖掘(Text Mining)也称作文本数据挖掘或从文本数据库中的知识发现,是指从非结构化文本信息中获取用户感兴趣或者有用的模式。其中被普遍认可的文本挖掘定义如下：文本挖掘是指从大量文本数据中抽取事先未知的、可理解的、最终可用的知识的过程,同时运用这些知识更好地组织信息以便将来参考。
面向语义关系发现的文本挖掘是目前的研究热点,其主要思想是通过对自然语言文本进行扫描和自动化处理,发现概念术语及概念术语间存在的语义关系。概念之间的各种语义关系是知识的重要表现形式,这些语义关系主要有上位关系／下位关系(hypernymy/hyponymy),部分整体关系(part-whole),因果关系(causality),同义关系(synonymy)、反义关系(antonymy)和推论关系(inference)等。从理论层面来看,面向语义关系发现的文本挖掘研究将使自然语言处理从词法分析、句法分析层面深入到语义分析层面；从应用层面来看,面向语义关系发现的文本挖掘研究可以为知识本体的自动或半自动构建提供理论和方法依据。
本文以军用飞机领域的语料为基础,以军用飞机领域概念体系间语义关系发现为研究对象,运用文本挖掘的处理思想和基本流程,结合自然语言处理、信息抽取、本体自动构建相关研究的理论和方法,对基于文本挖掘的语义关系发现进行了研究和探索,并开展了实验研究。主要工作和研究内容包括以下四个方面：
(1)面向语义关系发现的文本挖掘相关理论和研究综述。本文对国内外有关自然语言处理、文本挖掘、本体自动构建等方面的研究进展进行了介绍和综述,提出本文的研究方向和研究目标。在此基础上,对本文的研究内容和研究方法进行具体阐述。
(2)军用飞机领域文本处理语料的建设。以维基百科和CNKI数据库中与军用飞机相关的文章,作为本语料库的文本语料。本军用飞机语料库包括1951个术语,304篇文章,在其中抽取出3324个短句。该语料库的建设为本研究的实验提供了基础,也为后续的本体自动构建或其他相关工作提供了素材和研究支持。
(3)基于模板匹配的语义关系发现研究和实验。根据军用飞机领域的知识结构特点,提出军用飞机领域概念体系中包含的典型语义关系。为自动发现和识别这些语义关系,提出了结合编辑距离的模板匹配方法。该方法先在人工参与下训练一批语料,由此获得与语义关系类型相对应的关系模板。利用编辑距离对已获得的关系模板进行归纳匹配,再将其用于测试语料,以验证该方法的效果。
(4)基于复杂网络的语义关系研究和实验。梳理复杂网络理论知识,利用自然语言所具有的网络特征,运用复杂网络发现其中的语义关系。提出一种运用复杂网络和相关分析工具来辅助发现领域概念术语及其语义关系的方法。将术语和与之相关联的词语作为网络的节点,构造复杂网络,由此形成的各个社区就代表一个关系。将术语作为节点,发现的关系作为边,构造军用飞机领域概念体系的复杂网络,并对其进行分析。
Text Mining also known as text data mining, or knowledge discovery from textual databases, refers to obtain interesting or useful patterns for users from the unstructured text information. The generally recognized text mining is defined as follows:text mining is the process of extracting the unknown, understandable and available knowledge ultimately from a large number of text data, meanwhile uses this knowledge to organize information better for future reference.
Text mining about semantic relation discovery is a research focus; the main idea is to discovery terms and the semantic relationships between the terms with scanning the text of natural language and automated processing. The various semantic relations between concepts and language units is important form of knowledge, these semantic relations mainly are hypernymy/hyponymy, part-whole, causality, synonymy, antonymy, inference and so on. From the theorectical point, the semantic relations discovery in text mining research will make natural language processing from the lexical analysis, syntactic analysis deep into the senamtic analysis; from the application level, the senamtic relations discovery in text mining will provide the theory and method for automatic or semi-automatic ontology construnction.
Based on the military aircraft corpus, study the military aircraft domain concepts hierarchy semantic relations discovery, use text mining processing and basic process, combined natural language processing, information extraction, ontology automatic construction theory and related research method, based on semantic relations text mining conduct the research, explore and experiments. The main work and research include the following:
(1) Theory and research of semantic relations discovery in text mining. This article reviews the the natural language processing, text mining, automatic construction of ontology research aspects have been summarized.
(2) The construction of the military aircraft text processing corpus. Retrive the articles related to military aircraft articles in the Wikipedia and CNKI database. The military aircraft, including 1951 terms corpus,304 articles, in which the phrase extracted 3324. The corpus is the basic of the experiment, and it also provides materials and support for the ontology automatic construction, or other relevant work.
(3) The research and experiment of template matching for semantic discovery. According to the characteristics of military aircraft, propose specific semantic relationships. Training some corpus, thus obtained corresponds with the semantic relationship between the types of template. Use the edit distance has been summarized the relationship between the template matching, and the corpus for testing to verify the effectiveness of the method.
(4) The reaserch and experiment based on complex network. Use the natural language with the network characteristics to discover the semantic relationship. Use terms and words which have relation with terms as the node of the net to construct complex network. In this complex network, every community represents a relation. Next, use terms as node, use relation has been found as edge, construct the complex network of military aircraft domain concepts hierarchy, and analysis it.
引文
[1]陈肇雄,高庆狮.自然语言处理[J].计算机研究与发展,1989(11)：1-16.
[2]王灿辉,张敏,马少平.自然语言处理在信息检索中的应用综述[J].中文信息学报,2007,21(2)：35-45.
[3]孔佳薇.中文自然语言处理关键技术及主要软件产品介绍[OL], [2010-04-20], http://218.1.116.114/publish/portal2/tab227/info 147.htm.
[4]傅爱平,冯志伟.1994-1997年计算机语言学和自然语言处理研究综述[M].中国语言学年鉴,1995-1998：282-291.
[5]Robert Neches, Richard Fikes, Tim Finin, Thomas Gruber,Ramesh Patil, Ted Senator, and William R. Swartout. Enabling Technology for Knowledge Sharing[J]. AI Magazine,1991, 12(3):37-56.
[6]Thomas Gruber. A Translation Approach to Portable Ontology Specifications[J]. Knowledge Acquisition,1993,5(2):199-220.
[7]颜端武.面向知识服务的智能推荐系统研究[D].南京：南京理工大学,2007.
[8]Thomas R. Gruber. Toward principles for the design of ontologies used for knowledge sharing[J]. International Journal of Human-Computer Studies,1995,43:907-928.
[9]Mike Uschold, Martin King. Towards a Methodology for Building Ontologies[C]. International Joint Conference on Artificial Intelligence,1995:204-210.
[10]Mike Uschold. Building ontologies:towards a unified methodology[C].16th Annual conference of the British Computer Society Specialist Group on Expert Systems,1996: 267-275.
[11]Michael Gruninger, Mark S. Fox. Methodology for the design and evaluation of ontologies[C]. International Joint Conference on Artificial Intelligence,1995:53-58.
[12]Fernandez M, Gomez Perez A, PAZOS J and PAZOS A. Ontology of task and methods[J]. IEEE Intelligent System and Their Applications,1999,14:37-46.
[13]陈禹六.IDEF建模分析与设计方法[M].北京：清华大学出版社,1999.
[14]王侠,韩永印.本体构建研究[J].电脑与电信,2007,(11)：54-55.
[15]韩婕,向阳.本体构建研究综述[J].计算机应用与软件,2007,24(9)：21-23.
[16]杜文华,董慧.本体建设工具比较研究[J].情报杂志,2005(2)：5-7.
[17]仇利克.领域ontology的构建方法论及其存储研究[D].中国海洋大学,2006.
[18]WordNet[OL], [2010-01-15]. http://wordnet.princeton.edu/.
[19]徐力斌,刘宗田,周文,宋二伟.基于WordNet和自然语言处理技术的半自动领域本体构建[J].计算机科学,2007,34(6)：219-222.
[20]赵天忠,苗壮,张亚非,徐伟光,陆建江.基于WordNet重用的领域本体构建方法[J].系统仿真学报,2007,19(19)：4583-4586.
[21]Chang Chun, Lu Weilin. From Agricultwal thesaurus to ontology[C].5th AOS Workshop, 2004,04.
[22]唐爱民,真溱,樊静.基于叙词表的领域本体构建研究[J].现代图书情报技术,2005(4)：1-5.
[23]孙倩,万建成.基于叙词表的领域本体构建方法研究[J].计算机工程与设计,2007,28(20)：5054-5056.
[24]付佳佳.基于叙词表的领域本体建模研究[D].上海：华东师范大学,2006.
[25]任瑞娟.基于《中国分类主题词表》构建分布式Ontology[J].情报杂志,2008(8)：23-25.
[26]薛云,叶东毅,张文德.基于《中国分类主题词表》的领域本体构建研究[J].情报杂志,2007(3)：15-18.
[27]魏顺平,何克抗.基于文本挖掘的领域本体半自动构建方法研究——以教学设计学科领域本体建设为例[J].开放教育研究,2008,14(5)：95-101.
[28]Ah-hwee Tan. Text Mining:The state of the art and the challenges[C]. In Proceedings of the PAKDD 1999 Workshop on Knowledge Disocovery from Advanced Databases,1999.
[29]Corina Roxana Girju. Text Minging for Semantic Relations[D]. The University of Texas at Dallas,2002.
[30]谌志群,张国煊.文本挖掘研究进展[J].模式识别与人工智能,2005,18(1)：65-74.
[31]Hearst M A. Automatic Acquisition of Hyponyms from Large Text Corpora[C]. In Proceedings of the 14th International Conference on Computational Linguistics, Nantes, France.1992,539-545.
[32]Hearst M A. WordNet:An Electronic Lexical Database[M]. Cambridge, USA:MIT Press, 1998:131-151.
[33]Moldovan D I, Girju R. An Interactive Tool for the Rapid Development of Knowledge Bases[J]. International Journal on Artificial Intelligence Tools,2001,10(1):32-53.
[34]Girju R, Moldovan D. Text Mining for Causal Relation[C]. In Proceedings of the International Conference on Artificial Intelligence Research Society, Pensacola. USA,2002: 105-109.
[35]Harris Z. The Philosophy of Linguistics[M]. New York, USA:Oxford University Press, 1985:26-47.
[36]Lin D, Pantel P. DIRT-Discovery of Inference Rules from Text[C]. In Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Francisco, USA, 2001:323-328.
[37]Salton G, McGill M. Introduction to Modern Information Retrieval[M]. New York, USA: McGraw Hill,1983.
[38]胡冰,胡东军,马文超.文本挖掘研究及发展[J].电脑知识与技术,2008,4(4)：792-793.
[39]百度百科.语义学.[OL], [2010-01-15]. http://baike.baidu.com/view/158702.htm?fr= ala0 1 1.
[40]卢红君.语义关系概述[J].浙江工程学院学报,2003,20(3)：246-251.
[41]詹卫东.面向自然语言处理的大规模语义知识库研究述要[A].徐波、孙茂松编.中文信息处理若干重要问题[C].北京：科学出版社,2003.
[42]吴晓鸣.体育科研中唯物辩证法的主要方法论[J].兵团教育学院学报,1999,9(1)：72-75.
[43]FrameNet[OL], [2010-01-15]. http://framenet.icsi.berkeley.edu/.
[44]董振东,董强.知网[OL],[2010-01-15].http://www.keenage.com/zhiwang/c_zhiwang.html.
[45]于江生,刘扬,俞士汶.中文概念词典规格说明[J].汉语语言与计算学报,2003,13(2)：177-194.
[46]FrameNe、WordNe、VerbNet比较研究[J].贾君枝,董刚.情报科学,2007,25(11)：1682-1686.
[47]Collin Baker, Charles Fillmore, and John Low. The Berkeley FrameNet Project[EB/OL]. [2010-01-15], http://framenet.icsi.berkeley.edu/papers/ac198.pdf.
[48]田光明,刘艳玲.Framenet框架之间的关系分析[J].现代图书情报技术,2008(6)：1-5.
[49]Fillmore C J, Baker C F, and Sato H. Seeing Arguments through Transparent Structures [C]. In Proceedings of the Third International Conference on Language Resources and Evaluation,2002.
[50]贾君枝.FrameNet叙词表与传统叙词表语义关系比较研究[J].情报理论与实践,2006,29(5)：605-607,576.
[51]张刚,刘挺,卢志茂,李生.隐马尔可夫模型和HowNet在汉语词义标注中的应用[J].计算机研究,2003,(9)：1-4.
[52]王宏显,周强,邬晓钧.《知网》语义关系图的自动构建[J].中文信息学报,2008,22(5)：90-96.
[53]中文概念辞书(CCD) [OL], [2010-01-15].http://icl.pku.edu.cn/icl_groups/ccd.asp.
[54]徐健,张智雄,吴振新.实体关系抽取的技术方法综述[J].现代图书情报技术,2008(8)：18-23.
[55]Grishman R, Sundheim B, Message Understanding Conference-6:A Brief History [C]. In Proceedings of the 16h International Conference on Computational Linguistics (COLING-96), August 1996.
[56]黄晨.语义关系抽取发展现状及抽取方法的研究[J].福建电脑,2009(6)：45-46.
[57]Automatic Content Extraction (ACE) Evaluation[OL], [2010-01-17]. http://www. itl. nist.gov/iad/mig//tests/ace/.
[58]Automatic Content Extraction 2008 Evaluation(ACE08)[OL], [2010-01-17]. http://www. itl.nist.gov/iad/mig//tests/ace/2008/.
[59]Automatic Content Extraction 2008 Evaluation Plan (ACE08)[C]. In Proceedings of the ACE 2008 Evaluation,2008.
[60]C.Aone and M.Ramos-Santacruz. Rees:A Large-Scale Relation and Event Extraction System[C]. In Proceedings of the 6th Applied National Language Processing Conference, 2000:76-83.
[61]徐芬,王挺,陈火旺.基于SVM方法的中文实体关系抽取[C].第九届全国计算机语言学学术会议论文集,2007：497-502.
[62]张志田.无监督关系抽取方法研究[D].哈尔滨：哈尔滨工业大学,2007.
[63]Culotta A and Sorensen J. Dependency tree kernels for relation extraction[A]. ACL' 2004[C],2004,423-429.
[64]Fisher David, Soderland Stephen, McCarthy Joseph, et al. Description of the UMass systems as used for MUC-6[A]. In Proceedings of the 6th Message Understanding Conference[C],1995:127-140.
[65]Ellen Riloff.Automatically generating extraction patterns from untagged text[A].In Proceedings of the Thirteenth National Conference on Artificial Intelligence[C], 1996:148-161.
[66]奚斌.基于弱指导学习的实体间语义关系抽取研究[D].苏州：苏州大学,2008.
[67]Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines [M]. Cambridge University Press, Cambirdge University,2000:33-50.
[68]Tong Zhang. Regularized Winnow Methods[A]. In Advances in Neural Information Processing Systems [C],2001:703-709.
[69]庄成龙,钱龙华,周国栋.基于树核函数的实体语义关系抽取方法研究[J].中文信息学报,2009,23(1)：3-8.
[70]Shubin Zhao, Ralph Grishman. Extracting relations with integrated information using kernel methods[C]. Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics,2005:419-426.
[71]Zhou GuoDong, Su Jian, Zhang Jie, Zhang Min. Exploring various knowledge in relation extraction[C]. Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics,2005:427-434.
[72]Nanda Kambhatla. Combining lexical, syntactic and semantic features with Maximum Entropy models for extracting relations[C]. Proceedings of the ACL 2004 on Interactive poster and demonstration sessions,2004:178-181.
[73]车万翔,刘挺,李生.实体关系自动抽取[J].中文信息学报,2005,19(2)：1-6.
[74]黄瑞红,孙乐,冯元勇,黄云平.基于核方法的中文实体关系抽取研究[J].中文信息学报,2008,22(5)：102-107.
[75]Dmitry Zelenko,Chinatsu Aone, Anthony Richardella, Jaz K, Thomas Hofmann, Tomaso Poggio, John Shawe-taylor. Kernel Methods for Relation Extraction[J]. Journal of Machine Learning Research,2003,3:1083-1106.
[76]Aron Culotta, Jeffrey Sorensen. Dependency tree kernels for relation extraction[C]. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, 2004:423-429.
[77]Razvan C. Bunescu, Raymond J. Mooney. A Shortest Path Dependency Kernel for Relation Extraction[C]. Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing,2005:724-731.
[78]Min Zhang, Jie Zhang, Jian Su, Guodong Zhou. A Composite Kernel to Extract Relations between Entities with both Flat and Structured Features[C]. Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics,2006:825-832.
[79]陈锦秀,姬东鸿.基于图的半监督关系抽取[J].软件学报,2008,19(11)：2843-2852.
[80]Sergey Brin. Extracting patterns and relations from the world wide web[C]. In WebDB Workshop at 6th International Conference on Extending Database Technology,1998:172-183.
[81]Eugene Agichtein, Luis Gravano. Snowball:Extracting Relations from Large Plain-Text Collections[C]. In Proceedings of the 5th ACM International Conference on Digital Libraries, 2000,600-607.
[82]姜吉发,王树西.一种自举的二元关系和二元关系模式获取方法[J].中文信息学报,2005,19(2):71-77.
[83]Jinxiu Chen, Donghong Ji, Chew Lim Tan, Zhengyu Niu. Relation extraction using label propagation based semi-supervised learning[C]. Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics,2006:129-136.
[84]Takaaki Hasegawa, Satoshi Sekine, Ralph Grishman. Discovering Relations among Named Entities from Large Corpora Proc[C]. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics,2004:415-422.
[85]Hsin-Hsi Chen, Yung-Wei Ding, Shih-chung Tsai, Guo-wei Bian. Description of the NTU system Used for MET2[C]. In Proceedings of 7th Message Understanding Conference, 1998.
[86]Zhang Y M, Zhou J F. A Trainable Method for Extracting Chinese Entity Names and Their Relations[C]. In Proceedings of the Second Chinese Language Processing Workshop, Hong Kong,2000:66-72.
[87]短语结构语法[OL].[2010-02-01], http://baike.qiji.cn/Detailed/16986.html.
[88]刘挺,马金山.汉语自动句法分析的理论与方法[J].当代语言学,2009(2)：100-112.
[89]刘海涛.依存语法和机器翻译[J].语言文字应用,1997(3)：89-93.
[90]孙宏林,俞士汶.浅层句法分析方法概述[J].当代语言学,2000,2(2)：74-83.
[91]王强军,李芸,张普.信息技术领域术语提取的初步研究[J].术语标准化与信息技术[J],2003(1)：32-34.
[92]Fred J. Damerau. Generating and evaluating domain-oriented multi-word terms from texts[J]. Information Processing and Management,1993,29(4):433-447.
[93]Cohen, J. D. Highlights:Language- and Domain independent Automatic Indexing Terms for Abstracting[J]. Journal of American Soc. for Information Science,1995,46(3):162-174.
[94]Dunning T. Accurate Method for the Statistics of Surprise and Coincidence[C]. Computational Linguistics,1993,19(1):61-74.
[95]Frantzi, K T, Ananiadou S, Mima H.Automatic Recognition of Multi-Word Terms:the C-value/NC-value method[C]. International Journal on Digital Libraries,2000,3(2):115-130.
[96]Sue Ellen Wright, Gerhard Budin. Handbook of Terminology Management (Volume 2): Application-Oriented Terminology Management[M]. John Benjamins Publication Corpo-ration,2001.
[97]Patrick Pantel, Dekang Lin. A Statistical Corpus based Term Extractor[C].Ottawa, Canada:Lecture Notes in Artificial Intelligence,2001,36-46.
[98]Autonomy website[EB/OL].[2010]. http://www.autonomy.com/.
[99]张文静,梁颖红.术语抽取技术研究[J].信息技术,2008(3)：6-9.
[100]Justeson J S, Katz S M. Technical terminology:some linguistic properties and an algorithm for identification in text[J]. Natural Language Engineering,1995:224-265.
[101]胡文敏,何婷婷,张勇.基于卡方检验的汉语术语抽取[J].计算机应用,2007,27(12)：3019-3025.
[102]Fano, Robert M. In Transmission of information:a statistical theory of communications [M]. MIT Press,1961.
[103]Thian-HuatOng, Hsinchun Chen. Updateable PAT-Tree Approach to Chinese Key Phrase Extraction Using Mutual Information:A Linguistic Foundation for Knowledge Management[C]. Proceedings of the 2nd Asian Digital Library Conference,1999:63-84.
[104]Frank Smadja. Retrieving collocations from text:Xtract[J]. Computational Linguistics, 1993:110-129.
[105]张锋,许云,侯艳,樊孝忠.基于互信息的中文术语抽取系统[J].计算机应用研究,2005(5)：72-73.
[106]Pantel P, Lin D K. A statistical corpus-based term extractor[C]. Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence,2001,36-46.
[107]刘建舟,何婷婷,姬东鸿,刘晓华.基于开放语料的汉语术语的自动抽取[C].第二十届东方语言计算机处理国际学术会议,2003：43-49.
[108]Luo Z Y, Song R. An integrated method for Chinese unknown word extraction[C]. Proceedings of Third SIGHAN Workshop on Chinese.
[109]田怀凤.基于多策略的专业术语抽取处理技术的研究[J].计算机与现代化,2008,(12)：94-96.
[110]周俊生,戴新宇,尹存燕,陈家骏.基于层叠条件随机场模型的中文机构名自动识别[J].电子学报,2006,34(5)：804-809.
[111]王志强.基于条件随机域的中文命名实体识别研究[D].江苏：南京理工大学,2006.
[112]俞鸿魁,张华平,刘群,吕学强,施水才.基于层叠隐马尔可夫模型的中文命名实体识别[J].通信学报,2006,27(2)：87-94.
[113]何楠,毛新年,董远,王海拉.一种两阶段的中文命名实体识别方法[A].中国计算技术与语言问题研究——第七届中文信息处理国际会议论文集[C],北京：电子工业出版社出版,2007.
[114]刘建舟.术语自动抽取系统的设计及关键技术研究[D].上海：华中师范大学,2004.
[115]贺敏,龚才春,张华平,程学旗.一种基于大规模语料的新词识别方法[J].计算机 工程与应用,2007,43(21)：157-159.
[116]姜峰.基于条件随机场的中文分词研究[D].辽宁：大连理工大学,2006.
[117]岑咏华,韩哲,季培培.基于隐马尔科夫模型的中文术语识别研究[J].现代图书情报技术,2008,3(12)：54-58.
[118]Adwait RatnaParkhi. A simple introduetion to maximum entropy models for natural language proeessing[C]. Technical Report 97-08, Institute for Research in Cognitive Science, University of Pennsylvania,1997.
[119]Vapnik V. Statistical Learning Theory[M]. John Wiley,1998.
[120]王宁.模板处理在数字化文献中的应用[J].图书馆自动化,2001,(1)：43-45,49.
[121]王宁.模板处理在信息抽取过程中的应用[J].情报杂志,2000,19(6)：58-59,61.
[122]李芳,盛焕烨,张冬茉.多语种投资信息抽取系统的实现[J].上海交通大学学报,2004,38(1)：21-25.
[123]廖剑,李玉鑑.基于句子比较的英汉翻译模板自动提取算法[J].计算机工程与应用,2006,(25)：176-179.
[124]张素香,李蕾,谭咏梅.特定领域下关系模板的研究[J].北京邮电大学学报,2006,29(5)：79-83.
[125]叶娜,吴雪军,朱靖波,陈文亮.基于相似计算的信息抽取模板自动获取方法[J].第二届全国学生计算语言学研讨会,2004,2：434-439.
[126]林鸿飞,杨志豪,赵晶.中文文本的信息自动抽取和相似检索机制[J].小型微型计算机系统,2007,28(11)：2074-2079.
[127]丁晟春,刘逶迤,熊霞,梅健.基于领域本体和语块分析的信息抽取的研究与实现[J].情报学报,2010,29(1)：53-58.
[128]第三代智能分词系统3GWS[EB/OL]. http://blog.chinaunix.Net/u/28371/showart_215999.html,[2010-04-22].
[129]Maria Ruiz-Casado, Enrique Alfonseca and Pablo Castells. Automatic extraction of semantic relationships for WordNet by means of pattern learning fromWikipedia[C]. Natural Language Processing and Information Systems:10th International Conference on Applications of Natural Language to Information Systems, NLDB 2005, Alicante, Spain, June 15-17.
[130]李国臣,孟静.利用主语和谓语的句法关系识别谓语中心词[J].中文信息学报,2005,19(1)：1-7,41.
[131]郭艳华,周昌乐.一种汉语语句依存关系网协动生成方法研究[J].杭州电子工业学院学报,2000,20(4)：24-32.
[132]韦洛霞.复杂网络模型和方法[J].东莞理工学院学报,2004,11(4)：17-20.
[133]邬开俊,郑丽英,王铁君.复杂网络几种模型的比较与分析[J].科技信息,2006(3)：4-5.
[134]冯蕾,张宇光,唐丽.复杂网络理论在图书馆个性化推荐服务中的应用[J].情报理论与实践,2009,32(2)：69-71.
[135]王柏,吴巍,徐超群,吴斌.复杂网络可视化研究综述[J].计算机科学,2007,34(4)：17-23.
[136]Kamada T, Kawai S. An Algorit hm for Drawing General Undirected Graphs[C]. Information Processing Letters,1989,31:7-15.
[137]Fruchterman T M J, Reingold E M. Graph Drawing by Force Directed Placement[J]. Software Practice and Experience,1991,21(11):1129-1164.
[138]王曰芬,颜端武.信息获取与用户服务[M].北京：科学出版社,2010.