领域本体学习中术语及关系抽取方法的研究
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摘要
领域本体已经被广泛应用于知识工程和人工智能等多个领域,对企业的知识管理起着关键作用。特别是以产品为核心的离散制造企业,其主要的知识资产存在于产品和开发产品的流程中,对制造企业进行知识管理,需要对产品知识重新建模。本体可以形式化地表达问题,提供规范化、统一的知识表达形式,为知识的共享和重用提供模型。所以,在离散制造企业中引入领域本体进行知识管理至关重要。但是,人工构建领域本体费时费力,因此,自动或半自动构建领域本体成为研究的热点。
论文对领域本体学习中的两个关键问题,即术语及其关系的自动获取方法展开研究,以提高领域本体自动构建的有效性,为企业知识管理提供较好的模型和方法。
基于非结构化文本完成了以下几个方面的工作：
(1)提出基于信息熵和词频分布变化的无监督术语抽取方法。通过将信息熵结合到词频分布变化公式中进行术语抽取,且应用简单语言学规则过滤普通字符串。该方法对低频术语有较好的抽取效果,同时抽取出的术语结构更完整。
(2)提出一种基于条件随机场(Conditional Random Fields, CRF)和主动学习相结合的领域术语抽取方法。由于无监督机器学习方法抽取术语的精确率较低,而有监督方法要求有高质量大规模已标语料,人工标注领域语料代价大。引入主动学习方法,使用不确定性样本选择策略,计算CRF模型中的条件概率置信度,利用该置信度进行样本选择,使得通过较少的标注语料即可获得较高的精确率。
(3)提出基于多策略的术语关系抽取方法。针对术语关系类型的多样化问题,采用多方法结合的策略自动获取术语关系,主要研究同义关系和层级关系的获取,将基于规则的方法、基于统计的方法以及基于聚类的无监督机器学习方法结合,不同类型的关系采用不同的方法,使得对于层级关系的抽取获得了较好的性能。
(4)提出基于组合核函数和分布式元学习策略的实体关系抽取模型。将基于特征的平面核和基于句法的结构核进行组合,并结合分布式和元学习策略实现了中文实体关系抽取。实验结果表明,该方法的综合F-值比目前最好的系统高出近3个百分点。
为验证所提出方法的有效性,还构建了一个汽车领域本体实例。实验结果表明,本文所提出的基于文本的术语及关系抽取方法具有较高的性能,实现了领域本体构建过程中一定程度的自动化,同时也可以应用于词典编撰和文本摘要等其它领域。
Domain ontology has been applied to many fields, such as knowledge engineering, artificial intelligence and so on. It plays a key role in the knowledge management of enterprises, especially in the discrete manufacture enterprises that take the products as the core and whose knowledge property lies in the procedure of products and development. It is necessary to reconstruct product knowledge models for knowledge management in manufacture enterprises. Because the ontology can describe problems formally, provide normalized and uniform presentation forms, and offer models for knowledge sharing and reusing, domain ontology is introduced into the discrete manufacture enterprises for knowledge management. However, to construct domain ontology by domain experts will spend a lot of time and labor. Therefore, researchers have paid more and more attention to the research of constructing domain ontology automatically or semi-automatically.
Term and relation acquisition are important for domain ontology learning. This dissertation focuses on this research to improve the validity in constructing domain ontology automatically and provide effective methods for enterprise knowledge management.
This dissertation mainly concentrates on the following points to investigate the term and relation acquisition from unstructured texts:
(1) An unsupervised term extraction method based on information entropy and word frequency distributed variety is proposed. This method combines the information entropy and word frequency distributed variety and applies simple linguistic rules to filter character strings. The result shows that the method is more effective for extracting the terms with low frequency and can obtain the whole term structure.
(2) The domain term extraction method based on Conditional Random Field (CRF) combined with active learning strategy is proposed. On account of the poor performance of the unsupervised method and the expensive cost to obtain high-quality corpora in supervised method, this dissertation introduces the active learning strategy into the term extraction system based on CRF. The active learning method uses the uncertainty-based sampling strategy and selects samples using conditional probability given by CRF model. The active learning strategy can obtain better performance by less labeled corpus.
(3) A multi-strategies method is proposed for term relation extraction due to the diversification of term relation types. This dissertation focuses on the acquisition of synonymy relation and hierarchy relation. The rule-based method, the statistic-based method and the cluster-based unsupervised method for different relation types are integrated. It has achieved better performance for the hierarchy relation.
(4) This dissertation also puts forward the strategy to integrate the composite kernel method and the distributed meta-learning for Chinese entity relation extraction. Experiments are carried out based on the news field corpus. This method adopts the distributed meta-learning strategy, which uses the composite kernel that combines feature-based kernel and sentence structure based kernel. The result shows the F-score for entity relation extraction is improved nearly3percent.
The proposed approaches are validated by an instance of constructing the automotive field ontology. Experiments show that the approaches for extracting terms and relations from texts in this dissertation are efficient and support the semi-automatically construction of Chinese domain ontologies. In addition, these methods can be applied to other fields, such as dictionary compilation, text summarization.
论文对领域本体学习中的两个关键问题,即术语及其关系的自动获取方法展开研究,以提高领域本体自动构建的有效性,为企业知识管理提供较好的模型和方法。
基于非结构化文本完成了以下几个方面的工作：
(1)提出基于信息熵和词频分布变化的无监督术语抽取方法。通过将信息熵结合到词频分布变化公式中进行术语抽取,且应用简单语言学规则过滤普通字符串。该方法对低频术语有较好的抽取效果,同时抽取出的术语结构更完整。
(2)提出一种基于条件随机场(Conditional Random Fields, CRF)和主动学习相结合的领域术语抽取方法。由于无监督机器学习方法抽取术语的精确率较低,而有监督方法要求有高质量大规模已标语料,人工标注领域语料代价大。引入主动学习方法,使用不确定性样本选择策略,计算CRF模型中的条件概率置信度,利用该置信度进行样本选择,使得通过较少的标注语料即可获得较高的精确率。
(3)提出基于多策略的术语关系抽取方法。针对术语关系类型的多样化问题,采用多方法结合的策略自动获取术语关系,主要研究同义关系和层级关系的获取,将基于规则的方法、基于统计的方法以及基于聚类的无监督机器学习方法结合,不同类型的关系采用不同的方法,使得对于层级关系的抽取获得了较好的性能。
(4)提出基于组合核函数和分布式元学习策略的实体关系抽取模型。将基于特征的平面核和基于句法的结构核进行组合,并结合分布式和元学习策略实现了中文实体关系抽取。实验结果表明,该方法的综合F-值比目前最好的系统高出近3个百分点。
为验证所提出方法的有效性,还构建了一个汽车领域本体实例。实验结果表明,本文所提出的基于文本的术语及关系抽取方法具有较高的性能,实现了领域本体构建过程中一定程度的自动化,同时也可以应用于词典编撰和文本摘要等其它领域。
Domain ontology has been applied to many fields, such as knowledge engineering, artificial intelligence and so on. It plays a key role in the knowledge management of enterprises, especially in the discrete manufacture enterprises that take the products as the core and whose knowledge property lies in the procedure of products and development. It is necessary to reconstruct product knowledge models for knowledge management in manufacture enterprises. Because the ontology can describe problems formally, provide normalized and uniform presentation forms, and offer models for knowledge sharing and reusing, domain ontology is introduced into the discrete manufacture enterprises for knowledge management. However, to construct domain ontology by domain experts will spend a lot of time and labor. Therefore, researchers have paid more and more attention to the research of constructing domain ontology automatically or semi-automatically.
Term and relation acquisition are important for domain ontology learning. This dissertation focuses on this research to improve the validity in constructing domain ontology automatically and provide effective methods for enterprise knowledge management.
This dissertation mainly concentrates on the following points to investigate the term and relation acquisition from unstructured texts:
(1) An unsupervised term extraction method based on information entropy and word frequency distributed variety is proposed. This method combines the information entropy and word frequency distributed variety and applies simple linguistic rules to filter character strings. The result shows that the method is more effective for extracting the terms with low frequency and can obtain the whole term structure.
(2) The domain term extraction method based on Conditional Random Field (CRF) combined with active learning strategy is proposed. On account of the poor performance of the unsupervised method and the expensive cost to obtain high-quality corpora in supervised method, this dissertation introduces the active learning strategy into the term extraction system based on CRF. The active learning method uses the uncertainty-based sampling strategy and selects samples using conditional probability given by CRF model. The active learning strategy can obtain better performance by less labeled corpus.
(3) A multi-strategies method is proposed for term relation extraction due to the diversification of term relation types. This dissertation focuses on the acquisition of synonymy relation and hierarchy relation. The rule-based method, the statistic-based method and the cluster-based unsupervised method for different relation types are integrated. It has achieved better performance for the hierarchy relation.
(4) This dissertation also puts forward the strategy to integrate the composite kernel method and the distributed meta-learning for Chinese entity relation extraction. Experiments are carried out based on the news field corpus. This method adopts the distributed meta-learning strategy, which uses the composite kernel that combines feature-based kernel and sentence structure based kernel. The result shows the F-score for entity relation extraction is improved nearly3percent.
The proposed approaches are validated by an instance of constructing the automotive field ontology. Experiments show that the approaches for extracting terms and relations from texts in this dissertation are efficient and support the semi-automatically construction of Chinese domain ontologies. In addition, these methods can be applied to other fields, such as dictionary compilation, text summarization.
引文
[1]顾基发,唐锡晋.综合集成与知识科学[J].系统工程理论与实践,2002,10：2-6.
[2]余光胜.企业知识理论导向下的知识管理研究新进展[J].研究与发展管理,2005,17(3)：70-76.
[3]储节旺,周绍森,谢阳群,等.知识管理概论[M].北京：清华大学出版社,北京交通大学出版社,2005.
[4]江文年,杨建梅.企业知识管理方法论研究[M].科学出版社,2006.
[5]阮志斌,倪益华.基于本体面向制造企业的知识集成平台构建[J].精密制造与自动化,2006(1)：45-48.
[6]路甬祥.21世纪中国制造业面临的挑战与机遇[C].2004年中国机械工程学会年会论文集,2005(1)：1-17.
[7]冯国奇,崔东亮,王成恩,黄小原.一种基于网状版本模型的复杂产品设计数据管理方法研究[J].管理工程学报,2009,23(1)：82-87.
[8]刘柏嵩.基于本体的知识管理关键技术研究[J].情报学报,2005,24(1)：75-81.
[9]GRUBER T R. A translation approach to portable ontology specifications [J]. Appeared in Knowledge Acquisition,1993,5(2):199-220.
[10]BORST W N. Construction of Engineering Ontologies for Knowledge Sharing and Reuse [M]. Enschede:Universiteit Twente,1997.
[11]STUDER R, BENJAMINS V R, FENSEL D. Knowledge engineering:principles and methods [J]. Data and Knowledge Engineering,1998,25(1-2):161-197.
[12]USCHOLD M, GRUNINGER M. Ontology:principles, methods and applications [J]. Knowledge Engineering Review,1996,11(2):93-136.
[13]JASPER R, USCHOLD M. A framework for understanding and classifying ontology applications [C]. Proceedings of the IJCAI-99 Workshop on Ontologies and Problem-Solving Methods. Stockholm,1999.8.
[14]翟军,陈燕,林岩.汉语颜色词本体及其应用[J].情报学报,2011,30(11)：1226-1232.
[15]SURE Y, HITZLER P, EBERHART A. The semantic web in one day [J]. Intelligent System, 2005,20(3):85-87.
[16]DU X Y, LI M, WANG S. A survey on ontology learning research [J]. Journal of Software, 2006,17(9):1837-1847.
[17]GOMEZ-PEREZ A, MANZANO-MACHO D. A survey of ontology learning methods and techniques[C].Deliverable 1.5, OntoWeb Project,2003:1-86.
[18]SHAUNRELLE D, TIA W. Engineering knowledge [C]. Proceedings of the 42nd Annual Southeast Regional conference, Huntsville, Alabama,2004:406-407.
[19]ZHENG D Q, ZHAO T J, YU F, et al. Machine learning for automatic acquisition of Chinese linguistic ontology knowledge [C]. Machine Learning and Cybernetics,2005, 6:3728-3733.
[20]孔敬.本体学习：原理、方法与相关进展[J].情报学报,2006,25(6)：657-665.
[21]MODICA G, GAL A, JAMIL H M. The use of machine-generated ontologies in dynamic information seeking [C]. Proceedings of the 9th International Conference on Cooperative Information Systems. Heidelberg:Springer Berlin,2001:433-448.
[22]MAEDCHE A, STAAB S. The text-to-onto ontology learning environment [C]. Proceedings of Software Demonstration at ICCS-2000-Eight International Conference on Conceptual Structures. Darmstadt,2000.
[23]MISSIKOFF M, NAVIGLI R, VELARDI P. Integrated approach for web ontology learning and engineering [J]. Computer,2002,35(11):54-57.
[24]VOLZ R, OBERLE D, STAAB S, STUDER R. Ontolift prototype [C]. Deliverable 11, WonderWeb,2003.
[25]SHAMSFARD M, BARFOROUSH A A. Learning ontologies from natural language texts [J]. Human-computer studies,2004,60(1):17-63.
[26]薛中玉,李春梅,黄道雄.基于文本挖掘的本体自动构建系统架构解析[J].计算机技术与发展,2011,21(1)：100-103.
[27]温春,王晓斌,石昭祥.中文领域本体学习中术语的自动抽取[J].计算机应用研究,2009,26(7)：2652-2655.
[28]官莹莹.面向中文文本的本体学习方法研究[D].吉林：吉林大学,2009.
[29]杨芬.本体学习中概念和关系抽取方法研究[D].重庆：重庆大学,2010.
[30]于娟,党延忠.本体关系学习方法研究——概念特征词法,系统工程理论与实践,2012,7：1582-1590.
[31]梁吉震.基于领域概念知识的非分类关系学习研究[D].吉林大学,2012.
[32]中华人民共和国国家标准.GB/T10112-959.术语工作原则与方法[S].
[33]中华人民共和国国家标准.GB/T15237.1-2000.术语工作词汇第1部分：理论与应用[S]//全国术语标准化技术委员会.2000.
[34]中华人民共和国国家标准.GB/T16785-1997.术语工作概念与术语的协调[S]//全国术语标准化技术委员会.1997.
[35]JACQUEMIN C. Recycling terms into a partial parser [C]. Proceedings of NALP'94, 1994:113-118.
[36]JACQUEMIN C. Syntagmatic and paradigmatic representations of term variation [C]. Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics,1999:341-348.
[37]IDO D G, KEN C. Termight:identifying and translating technical terminolog [C]. Proceedings of the 4th Conference on Applied Natural Language,1994:34-40.
[38]LAURLSTON A. Automatic recognition of complex terms:problems and the "TERMINO" solution [J]. Terminology,1994,1(1):147-170.
[39]JUSTESON J, KATZ S. Technical terminology:some linguistic properties and an algorithm for identification in text [J]. Natural Language Engineering,1995, 1(1):9-27.
[40]HEID U, JAUSS S, KRUGER K, et al. Term extraction with standard tools for corpus exploration:experience from german [C]. Proceedings of the 4th International Congress on Terminology and Knowledge Engineering(TKE,96),1996:139-150.
[41]BOURIGAULT D, GONZALEZ-MULLIER I, GROS L C. A natural language processing tool for terminology extraction [C]. Proceedings of the 7th EURALEX International Congress on Lexicography,1996:771-779.
[42]NAULLEAU E. Profile-guided terminology extraction [C]. Proceedings of the TKE'99: Terminology and Knowledge Engineering,1999:222-240.
[43]BEATRICE D, ERIC G, JEAN M L. Towards automatic extraction of monolingual and bilingual terminology [C]. Proceedings of the 15th Conference on Computational Linguistics, Japan,1994:515-521.
[44]DIDIER B. Surface grammatical analysis for the extraction of terminological noun phrases [C]. Proceedings of the 14th Conference on Computational Linguistics, Stroudsburg,1992:977-981.
[45]ENGUEHARD C, PANTERA L. Automatic terminology [J]. Quantitative Linguistics, 1994,2(1):27-32.
[46]ZHANG Z Q, JOSE I, CHRISTOPHER B, et al. A comparative evaluation of term recognition algorithms [C]. Proceedings of the 6th International Language Resources and Evaluation(LREC 08),2008:2108-2113.
[47]刘桃,刘秉权,徐志明,等.领域术语自动抽取及其在文本分类中的应用[J].电子学报,2007,35(2)：328-332.
[48]王强军.基于动态流通语料库(DCC)的信息技术领域新术语自动提取研究[D].北京：北京语言大学,2003.
[49]DAILLE B. Study and implementation of combined techniques for automatic extraction of terminology [C]. Proceedings of 32th Annual Meeting of the Association for Computational Linguistics,1994:29-36.
[50]FRANTZI K T, ANANIADOU S. Extracting nested collocations [C]. Proceedings of the 16th International Conference on Computational Linguistics(COLING'96),1996: 41-46.
[51]Gensen Web. http://gensen. dl. itc. u-tokyo. ac. jp/gensenweb_cn. html.
[52]OCHOA J L, ALMEMA A, HERNONDEZ-ALCARAZ M L, et al. Learning morphosyntactic patterns for multiword term extraction [J]. Scientific Research and Essays,2011, 6(26):5563-5578.
[53]VU T, AW A T, ZHANG M. Term extraction through unithood and termhood unification [C]. Proceedings of the 3rd International Joint Conference on Natural Language Processing, Hyderabad, India,2008.
[54]穗志方.信息科学技术领域术语自动识别策略[C].第二届中日自然语言处理专家研讨会第二届中日自然语言处理专家研讨会,2002.
[55]SUI Z F, CHEN Y R, WEI Z C. Automatic recognition of Chinese scientific and technological key phrases using integrated linguistic knowledge [C]. Proceedings of Natural Language Processing and Knowledge Engineering,2003.
[56]杜波,田怀凤,王立等.基于多策略的专业领域术语抽取器的设计[J].计算机工程,2005,31(14)：159-160.
[57]何婷婷,张勇.基于质子串分解的中文术语自动抽取[J].计算机工程,2006,32(23)：188-190.
[58]ZAN H Y, DUAN G C, FAN M. Single word term extraction bilingual semantic lexicon-based approach [C]. Proceedings of International Conference on Natural Computation,2007:451-456.
[59]张锋,许云,侯艳,等.基于互信息的中文术语抽取系统[J].计算机应用研究,2005,22(5)：72-73.
[60]梁颖红,张文静,张有承.C值和互信息相结合的术语抽取[J].计算机应用与软件,2010,27(4)：108-110.
[61]郑家恒,卢娇丽.关键词抽取方法的研究[J].计算机工程,2005,31(18)：194-196.
[62]周浪,张亮,冯冲等.基于词频分布变化统计的术语抽取方法[J].计算机科学,2009,36(5)：177-180.
[63]YANG Y H, LU Q, ZHAO T J. Chinese term extraction using minimal resources [C]. Proceedings of the 22nd International Conference on Computational Linguistics, Manchester,2007:1033-1040.
[64]YANG Y H, ZHAO T J, LU Q, et al. Chinese term extraction using different types of relevance [C]. Proceedings of the ACL-IJCNLP 2009, Singapore,2009:213-216.
[65]YANG Y H, LU Q, ZHAO T J. A delimiter-based general approach for Chinese term extraction [J]. American Society for Information Science and Technology,2010, 61(1):111-125.
[66]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.
[67]于娟.基于文本的领域本体学习方法及其应用研究[D].大连：大连理工大学,2010.6.
[68]李丹.特定领域中文术语抽取[D].大连：大连理工大学,2011.
[69]李卫.领域知识的获取[D].北京：北京邮电大学,2008.
[70]游宏梁,张巍,沈钧毅,等.一种基于加权投票的术语自动识别方法[J].中文信息学报,2011,25(3)：9-16.
[71]周浪,史树敏,冯冲,等.基于多策略融合的中文术语抽取方法[J].情报学报,2010,29(3)：460-467.
[72]姜韶华,党延忠.自动提取含字母词语的领域新术语的研究[J].计算机工程,2007,33(2)：47-49.
[73]JI L, SUM M, LU Q, et al. Chinese terminology extraction using window-based contextual information [C]. Proceedings of Computational Linguistics and Intelligent Text Processing,2007,4394:62-74.
[74]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(12)：54-48.
[75]刘豹,张桂平,蔡东风.基于统计和规则相结合的科技术语自动抽取研究[J].计算机工程与应用,2008,44(23)：147-150.
[76]ZHENG D Q, ZHAO T J, YANG J. Research on domain term extraction based on conditional random fields [C]. Proceedings of the ICCPOL 2009,5459:290-296.
[77]章承志.基于多层术语度的一体化术语抽取研究[J].情报学报,2011,28(3)：275-285.
[78]ZHANG X, SONG Y, FANG A C. Term recognition using conditional random fields [C]. Proceedings of Natural Language Processing and Knowledge Engineering, Beijing, 2010:1-6.
[79]潘渭,顾宏斌.采用改进重采样和BRF方法的定义抽取研究[J].中文信息学报,2011,25(3)：30-37.
[80]HEARST M A. Automated discovery of wordnet relations, to appear in WordNet:an electronic lexical database [M]. MIT Press,1998.
[81]PANTELP, PENNACCHIOTTI M. Espresso:leveraging generic patterns for automatically harvesting semantic relations [C]. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, Sydney, 2006:113-120.
[82]SHIH C W, CHEN M Y, CHU H C, et al. Enhancement of domain ontology construction using a crystallizing approach [J]. Expert Systems with Applications,2011,38(6): 7544-7557.
[83]GAETA M, ORCUOLI F, PAOLOZZI S, et al. Ontology extraction for knowledge reuse: the e-learning perspective [J]. Systems Man and Cybernetics, Part A:Systems and Humans,2011,41(4):798-809.
[84]TAKAMATSU S, SATO I, NAKAGAWA H. Probabilistic matrix factorization leveraging contexts for unsupervised relation extraction [J]. Advances in Knowledge Discovery and Data Mining,2011,6634:87-99.
[85]GONZALEZ E, TURMO J. Unsupervised relation extraction by massive clustering[C]. Proceedings of the 9th IEEE International Conference on Data Mining, Miami,2009: 782-787.
[86]CIARAMITAM, GANGEMI A, RATSCH E, et.1 Unsupervised learning of semantic relationships between concepts of a molecular biology Ontology [C]. In Proceedings ofthel9th International Joint Conference on Artificial Intelligence(IJCAI), Edinburgh, Scotland,UK,2005:659-664.
[87]MAEDCHE A, STAAB S. Semi-automatic engineering of ontologies from text[C]. Proceedings of the 12th International Conference on Software Engineering and Knowledge Engineering. Chicago,2000.
[88]谌贻荣,陆勤,李文捷,等.中文核心领域本体构建的一种改进方法[J].中文信息学报,2010,24(1)：48-53.
[89]张新.基于中文科技论文的本体交互式构建方法研究[D].大连：大连理工大学,2006.11.
[90]陈珂.构造领域本体概念关系的自动抽取[D].上海：上海交通大学,2008.1.
[91]王磊,周宽久,仇鹏,等.领域本体自动构建研究[J].情报学报,2010,29(1)：30-33.
[92]程晓,郑德权,杨宇航,等.面向半结构化文本的领域本体关系抽取[C].第十届全国计算语言学学术会议,中国山东烟台,2009.
[93]何婷婷,张小鹏.特定领域本体自动构造方法[J].计算机工程,2007,33(22)：235-237.
[94]BERLAND M, CHARNIAK E. Finding parts in very large corporat[C]. Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, College Park, Maryland,1999:57-64.
[95]WIDDOWS D, DOROW B. A graph model for unsupervised lexical acquisition [C]. Proceedings of the 19th International Conference on Computational Linguistics, 2002:1093-1099.
[96]ETZIONI O, CAFARELLA M, DOWNEY D, et al. Unsupervised named-entity extraction from the web:an experimental study [J]. Artificial Intelligence,2005,165(1): 91-134.
[97]NAKAYA N, KURUREMATSU M, YAMAGUCHI T. A domain ontology development environment using a MRD and text corpus [C]. Proceedings of the Joint Conf. on Knowledge Based Software Engineering,2002:909-912.
[98]WordNet. http://wordnet.princeton.edu/, (Accessed on Oct 8,2006).
[99]NAVIGLI R, VELARDI P. Learning domain ontologies from document warehouses and dedicated web sites [J]. Computational Linguistics,2004,30(2):151-179.
[100]POESIO M, ALMUHAREB A. Identifying concept attributes using a classifier [C]. Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition, Ann Arbor, 2005:18-27.
[101]NANCY A. Chinehor. Overview of MUC-7/MET-2. Proceedings of the Seventh Message Understanding Conference(MUC-7). Fairfax. Virginia,1998.
[102]ACE 2004. The Automatic Content Extraction (ACE) Projects,2007. http://www.1dc.upenn.edu/Projects/ACE/.
[103]ACE 2005. The Automatic Content Extraction(ACE) Projects,2007. http://www.1dc.upenn.edu/Projects/ACE/
[104]KAMBHATLA N. 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, Barcelona, Spain,2004:178-181.
[105]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.
[106]董静,孙乐,冯元勇,等.中文实体关系抽取中的特征选择研究[J].中文信息学报,2007,21(4)：80-85.
[107]黄鑫,朱巧明,钱龙华,等.基于特征组合的中文实体关系抽取[J].微电子学与计算机,2010,27(4)：198-200.
[108]刘克彬,李芳,刘磊.基于核函数中文关系自动抽取系统的实验[J].计算机研究与发展,2007,44(8)：1406-1411.
[109]HUANG R H, SUN L, FENG Y Y. Study of kernel-based methods for Chinese relation extraction [J]. Information retrieval technology,2008,4993:598-604.
[110]ZHOU G D, QIAN L H, FAN J X. Tree kernel-based semantic relation extraction with rich syntactic and semantic information[J]. Information Science,2010,180(8): 1313-1325.
[111]虞欢欢,钱龙华,周国栋,等.基于合一句法和实体语义树的中文语义关系抽取[J].中文信息学报,2010,24(5)：17-23.
[112]POESIO M, BARBU E, GIULIANO C, et al. Supervised relation extraction for ontology learning from text based on a cognitively plausible model of relations [C]. Proceedings of the Italy 3rd Workshop on Ontology Learning and Population, Patras,2008:1-5.
[113]FERNANDEZ-LOPEZ M. Overview of methodologies for building ontologies[C]. Proceedings of the IJCA1-99 Workshop on Ontologies and Problem-Solving Methods (KRE5) Stockholm, Sweden,1999.8.
[114]GRUNINGER M, FOX M S. Methodology for the design and evaluation of ontologies [C]. Workshop on Basic Ontological Issues in Knowledge Sharing, IJCAI-95, Montreal,1995.
[115]BEMARAS A, LARESGOITI I, CORERA J. Building and reusing ontologies for electrical network applications [C]. Proceedings of the European Conference on Artificial Intelligence, Budapest, Hungary:John Wiley and Sons,1996:298-302.
[116]FERNANDEZ M, GOMEZ-PEREZ A, JURISTO N. Methontology:from ontological art towards ontological engineering [J]. Symposium on ontological Engineering of AAAI, Stanford (California),1997:24-26.
[117]SWARTOUT B, PATIL R, KNIGHT K, et al. Toward distributed use of large-scale ontologies [J]. Symposium on Ontological Engineering of AAAI. Stanford(California),1997:138-148.
[118]NOY N F, MCGUINNESS D L. Ontology development 101:a guide to creating your first ontology,2001-08. http://protege.stanford. edu/publications/ontology_development/ontology101.pd f, Accessed:2008.02
[119]黄伟.本体构建与语义集成研究[D].南京：东南大学计算机应用专业,2005.
[120]http://www.idef.com/idef5.html [EB/OL]. Accessed:2008.02
[121]HORRIDGE M, KNUBLAUCH H, RECTOR A, er al. A practical guide to building OWL ontology using the protege-OWL plugin and CO-ODE tools [EB]. The University of Manchester, 2004.8.
[122]SMITH M K, WELTY C, MCGUINNESS D L. OWL web ontology language guide [EB/OL]. http://www.w3.org/TR/2004/REC-owl-guide-20040210/, (Accessed on Jan.1,2005). Germany, August 15th,2000.
[123]BECHHOFER S, HARMELEN F V, HENDLER J. OWL web ontology language reference [EB/OL]. http://ww.w3.org/TR/2004/REC-owl-ref-20040210/, (Accessed on Jan.1,2005).
[124]OMELAYENKO B. Learning of ontologies for the web:the analysis of existent approaches[C]. Proceedings of the International Workshop on Web Dynamics, London, 2001.
[125]HUANG D G, TONG D Q, LUO Y Y. HMM revises low marginal probability by CRF for Chinese word segmentation [C]. Proceedings of CIPS-SIGHAN Joint Conference on Chinese Processing, Beijing,2010:216-220.
[126]FRAKES W B, BAEZA-YATES R. Information Retrieval Data Structures & Algorithms[M]. Prentice Hall PTR,1992:66-82.
[127]仁禾,曾隽芳.一种基于信息熵的中文高频词抽取算法[J].中文信息学报.2006,20(5): 40-43.
[128]LAFFERTY J, MCCALLUM A, PEREIRA F. Conditional random fields:probabilistic models for segmenting and labeling sequence data [C]. Proceedings of the International Conference on Machine Learning, Williams,2001:282-289.
[129]聂规划,傅魁.基于Web的中文本体学习研究[J].情报杂志,2008,6：13-16.
[130]LEWIS D D, GALE W A. A sequential algorithm for training text classifiers[C]. Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,1994:3-12.
[131]LTP platform, http://ir.hit.edu.cn/ltp/.
[132]MCDONALD R, PEREIRA F, RIBAROV K, et al. Non-projective dependency parsing using spanning tree algorithms [C]. Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Vancouver,2005:523-530.
[133]COLLINS M, DUFFY N. Convolution kernels for natural language [C]. Proceedings of the NIPS'2001,2001:625-632.
[134]ZHANG M, ZHANG J, SU J, et al. 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.
[135]MICHAEL W. An introduction to multi agent systems [M]. Chichester, England:John Wiley & Sons,2002.
[136]LIAO LJ, XU KQ, LIAO SS. Constructing intelligent and open mobile commerce using a semantic web approach [J]. Journal of Information Science,2005,31(5): 407-419.
[137]STONE P, VELOSO M. Multiagent systems:a survey from a machine learning perspective [J]. Autonomous Robots,2000,8(3):345-383.
[138]PRODROMIDIS A L, CHAN P K, STOLFO S J. Meta-learning in distributed data mining systems:issues and approaches [C]. Proceedings of Distributed Data Mining, Menlo Park, California, USA,2000:81-114.
[139]ZHANG J, OUYANG Y, LI W J, et al. A novel composite kernel approach to Chinese entity relation extraction [C]. Proceedings of the ICCPOL 2009,2009,5459: 236-247.
[140]中华人民共和国国务院.国函(1987)142号,1987.8.12.
[141]于娟,马金平,李永.基于Web本体语言OWL的知识表示[J].计算机工程与设计,2006,27(22)：4256-4357.
[2]余光胜.企业知识理论导向下的知识管理研究新进展[J].研究与发展管理,2005,17(3)：70-76.
[3]储节旺,周绍森,谢阳群,等.知识管理概论[M].北京：清华大学出版社,北京交通大学出版社,2005.
[4]江文年,杨建梅.企业知识管理方法论研究[M].科学出版社,2006.
[5]阮志斌,倪益华.基于本体面向制造企业的知识集成平台构建[J].精密制造与自动化,2006(1)：45-48.
[6]路甬祥.21世纪中国制造业面临的挑战与机遇[C].2004年中国机械工程学会年会论文集,2005(1)：1-17.
[7]冯国奇,崔东亮,王成恩,黄小原.一种基于网状版本模型的复杂产品设计数据管理方法研究[J].管理工程学报,2009,23(1)：82-87.
[8]刘柏嵩.基于本体的知识管理关键技术研究[J].情报学报,2005,24(1)：75-81.
[9]GRUBER T R. A translation approach to portable ontology specifications [J]. Appeared in Knowledge Acquisition,1993,5(2):199-220.
[10]BORST W N. Construction of Engineering Ontologies for Knowledge Sharing and Reuse [M]. Enschede:Universiteit Twente,1997.
[11]STUDER R, BENJAMINS V R, FENSEL D. Knowledge engineering:principles and methods [J]. Data and Knowledge Engineering,1998,25(1-2):161-197.
[12]USCHOLD M, GRUNINGER M. Ontology:principles, methods and applications [J]. Knowledge Engineering Review,1996,11(2):93-136.
[13]JASPER R, USCHOLD M. A framework for understanding and classifying ontology applications [C]. Proceedings of the IJCAI-99 Workshop on Ontologies and Problem-Solving Methods. Stockholm,1999.8.
[14]翟军,陈燕,林岩.汉语颜色词本体及其应用[J].情报学报,2011,30(11)：1226-1232.
[15]SURE Y, HITZLER P, EBERHART A. The semantic web in one day [J]. Intelligent System, 2005,20(3):85-87.
[16]DU X Y, LI M, WANG S. A survey on ontology learning research [J]. Journal of Software, 2006,17(9):1837-1847.
[17]GOMEZ-PEREZ A, MANZANO-MACHO D. A survey of ontology learning methods and techniques[C].Deliverable 1.5, OntoWeb Project,2003:1-86.
[18]SHAUNRELLE D, TIA W. Engineering knowledge [C]. Proceedings of the 42nd Annual Southeast Regional conference, Huntsville, Alabama,2004:406-407.
[19]ZHENG D Q, ZHAO T J, YU F, et al. Machine learning for automatic acquisition of Chinese linguistic ontology knowledge [C]. Machine Learning and Cybernetics,2005, 6:3728-3733.
[20]孔敬.本体学习：原理、方法与相关进展[J].情报学报,2006,25(6)：657-665.
[21]MODICA G, GAL A, JAMIL H M. The use of machine-generated ontologies in dynamic information seeking [C]. Proceedings of the 9th International Conference on Cooperative Information Systems. Heidelberg:Springer Berlin,2001:433-448.
[22]MAEDCHE A, STAAB S. The text-to-onto ontology learning environment [C]. Proceedings of Software Demonstration at ICCS-2000-Eight International Conference on Conceptual Structures. Darmstadt,2000.
[23]MISSIKOFF M, NAVIGLI R, VELARDI P. Integrated approach for web ontology learning and engineering [J]. Computer,2002,35(11):54-57.
[24]VOLZ R, OBERLE D, STAAB S, STUDER R. Ontolift prototype [C]. Deliverable 11, WonderWeb,2003.
[25]SHAMSFARD M, BARFOROUSH A A. Learning ontologies from natural language texts [J]. Human-computer studies,2004,60(1):17-63.
[26]薛中玉,李春梅,黄道雄.基于文本挖掘的本体自动构建系统架构解析[J].计算机技术与发展,2011,21(1)：100-103.
[27]温春,王晓斌,石昭祥.中文领域本体学习中术语的自动抽取[J].计算机应用研究,2009,26(7)：2652-2655.
[28]官莹莹.面向中文文本的本体学习方法研究[D].吉林：吉林大学,2009.
[29]杨芬.本体学习中概念和关系抽取方法研究[D].重庆：重庆大学,2010.
[30]于娟,党延忠.本体关系学习方法研究——概念特征词法,系统工程理论与实践,2012,7：1582-1590.
[31]梁吉震.基于领域概念知识的非分类关系学习研究[D].吉林大学,2012.
[32]中华人民共和国国家标准.GB/T10112-959.术语工作原则与方法[S].
[33]中华人民共和国国家标准.GB/T15237.1-2000.术语工作词汇第1部分：理论与应用[S]//全国术语标准化技术委员会.2000.
[34]中华人民共和国国家标准.GB/T16785-1997.术语工作概念与术语的协调[S]//全国术语标准化技术委员会.1997.
[35]JACQUEMIN C. Recycling terms into a partial parser [C]. Proceedings of NALP'94, 1994:113-118.
[36]JACQUEMIN C. Syntagmatic and paradigmatic representations of term variation [C]. Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics,1999:341-348.
[37]IDO D G, KEN C. Termight:identifying and translating technical terminolog [C]. Proceedings of the 4th Conference on Applied Natural Language,1994:34-40.
[38]LAURLSTON A. Automatic recognition of complex terms:problems and the "TERMINO" solution [J]. Terminology,1994,1(1):147-170.
[39]JUSTESON J, KATZ S. Technical terminology:some linguistic properties and an algorithm for identification in text [J]. Natural Language Engineering,1995, 1(1):9-27.
[40]HEID U, JAUSS S, KRUGER K, et al. Term extraction with standard tools for corpus exploration:experience from german [C]. Proceedings of the 4th International Congress on Terminology and Knowledge Engineering(TKE,96),1996:139-150.
[41]BOURIGAULT D, GONZALEZ-MULLIER I, GROS L C. A natural language processing tool for terminology extraction [C]. Proceedings of the 7th EURALEX International Congress on Lexicography,1996:771-779.
[42]NAULLEAU E. Profile-guided terminology extraction [C]. Proceedings of the TKE'99: Terminology and Knowledge Engineering,1999:222-240.
[43]BEATRICE D, ERIC G, JEAN M L. Towards automatic extraction of monolingual and bilingual terminology [C]. Proceedings of the 15th Conference on Computational Linguistics, Japan,1994:515-521.
[44]DIDIER B. Surface grammatical analysis for the extraction of terminological noun phrases [C]. Proceedings of the 14th Conference on Computational Linguistics, Stroudsburg,1992:977-981.
[45]ENGUEHARD C, PANTERA L. Automatic terminology [J]. Quantitative Linguistics, 1994,2(1):27-32.
[46]ZHANG Z Q, JOSE I, CHRISTOPHER B, et al. A comparative evaluation of term recognition algorithms [C]. Proceedings of the 6th International Language Resources and Evaluation(LREC 08),2008:2108-2113.
[47]刘桃,刘秉权,徐志明,等.领域术语自动抽取及其在文本分类中的应用[J].电子学报,2007,35(2)：328-332.
[48]王强军.基于动态流通语料库(DCC)的信息技术领域新术语自动提取研究[D].北京：北京语言大学,2003.
[49]DAILLE B. Study and implementation of combined techniques for automatic extraction of terminology [C]. Proceedings of 32th Annual Meeting of the Association for Computational Linguistics,1994:29-36.
[50]FRANTZI K T, ANANIADOU S. Extracting nested collocations [C]. Proceedings of the 16th International Conference on Computational Linguistics(COLING'96),1996: 41-46.
[51]Gensen Web. http://gensen. dl. itc. u-tokyo. ac. jp/gensenweb_cn. html.
[52]OCHOA J L, ALMEMA A, HERNONDEZ-ALCARAZ M L, et al. Learning morphosyntactic patterns for multiword term extraction [J]. Scientific Research and Essays,2011, 6(26):5563-5578.
[53]VU T, AW A T, ZHANG M. Term extraction through unithood and termhood unification [C]. Proceedings of the 3rd International Joint Conference on Natural Language Processing, Hyderabad, India,2008.
[54]穗志方.信息科学技术领域术语自动识别策略[C].第二届中日自然语言处理专家研讨会第二届中日自然语言处理专家研讨会,2002.
[55]SUI Z F, CHEN Y R, WEI Z C. Automatic recognition of Chinese scientific and technological key phrases using integrated linguistic knowledge [C]. Proceedings of Natural Language Processing and Knowledge Engineering,2003.
[56]杜波,田怀凤,王立等.基于多策略的专业领域术语抽取器的设计[J].计算机工程,2005,31(14)：159-160.
[57]何婷婷,张勇.基于质子串分解的中文术语自动抽取[J].计算机工程,2006,32(23)：188-190.
[58]ZAN H Y, DUAN G C, FAN M. Single word term extraction bilingual semantic lexicon-based approach [C]. Proceedings of International Conference on Natural Computation,2007:451-456.
[59]张锋,许云,侯艳,等.基于互信息的中文术语抽取系统[J].计算机应用研究,2005,22(5)：72-73.
[60]梁颖红,张文静,张有承.C值和互信息相结合的术语抽取[J].计算机应用与软件,2010,27(4)：108-110.
[61]郑家恒,卢娇丽.关键词抽取方法的研究[J].计算机工程,2005,31(18)：194-196.
[62]周浪,张亮,冯冲等.基于词频分布变化统计的术语抽取方法[J].计算机科学,2009,36(5)：177-180.
[63]YANG Y H, LU Q, ZHAO T J. Chinese term extraction using minimal resources [C]. Proceedings of the 22nd International Conference on Computational Linguistics, Manchester,2007:1033-1040.
[64]YANG Y H, ZHAO T J, LU Q, et al. Chinese term extraction using different types of relevance [C]. Proceedings of the ACL-IJCNLP 2009, Singapore,2009:213-216.
[65]YANG Y H, LU Q, ZHAO T J. A delimiter-based general approach for Chinese term extraction [J]. American Society for Information Science and Technology,2010, 61(1):111-125.
[66]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.
[67]于娟.基于文本的领域本体学习方法及其应用研究[D].大连：大连理工大学,2010.6.
[68]李丹.特定领域中文术语抽取[D].大连：大连理工大学,2011.
[69]李卫.领域知识的获取[D].北京：北京邮电大学,2008.
[70]游宏梁,张巍,沈钧毅,等.一种基于加权投票的术语自动识别方法[J].中文信息学报,2011,25(3)：9-16.
[71]周浪,史树敏,冯冲,等.基于多策略融合的中文术语抽取方法[J].情报学报,2010,29(3)：460-467.
[72]姜韶华,党延忠.自动提取含字母词语的领域新术语的研究[J].计算机工程,2007,33(2)：47-49.
[73]JI L, SUM M, LU Q, et al. Chinese terminology extraction using window-based contextual information [C]. Proceedings of Computational Linguistics and Intelligent Text Processing,2007,4394:62-74.
[74]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(12)：54-48.
[75]刘豹,张桂平,蔡东风.基于统计和规则相结合的科技术语自动抽取研究[J].计算机工程与应用,2008,44(23)：147-150.
[76]ZHENG D Q, ZHAO T J, YANG J. Research on domain term extraction based on conditional random fields [C]. Proceedings of the ICCPOL 2009,5459:290-296.
[77]章承志.基于多层术语度的一体化术语抽取研究[J].情报学报,2011,28(3)：275-285.
[78]ZHANG X, SONG Y, FANG A C. Term recognition using conditional random fields [C]. Proceedings of Natural Language Processing and Knowledge Engineering, Beijing, 2010:1-6.
[79]潘渭,顾宏斌.采用改进重采样和BRF方法的定义抽取研究[J].中文信息学报,2011,25(3)：30-37.
[80]HEARST M A. Automated discovery of wordnet relations, to appear in WordNet:an electronic lexical database [M]. MIT Press,1998.
[81]PANTELP, PENNACCHIOTTI M. Espresso:leveraging generic patterns for automatically harvesting semantic relations [C]. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, Sydney, 2006:113-120.
[82]SHIH C W, CHEN M Y, CHU H C, et al. Enhancement of domain ontology construction using a crystallizing approach [J]. Expert Systems with Applications,2011,38(6): 7544-7557.
[83]GAETA M, ORCUOLI F, PAOLOZZI S, et al. Ontology extraction for knowledge reuse: the e-learning perspective [J]. Systems Man and Cybernetics, Part A:Systems and Humans,2011,41(4):798-809.
[84]TAKAMATSU S, SATO I, NAKAGAWA H. Probabilistic matrix factorization leveraging contexts for unsupervised relation extraction [J]. Advances in Knowledge Discovery and Data Mining,2011,6634:87-99.
[85]GONZALEZ E, TURMO J. Unsupervised relation extraction by massive clustering[C]. Proceedings of the 9th IEEE International Conference on Data Mining, Miami,2009: 782-787.
[86]CIARAMITAM, GANGEMI A, RATSCH E, et.1 Unsupervised learning of semantic relationships between concepts of a molecular biology Ontology [C]. In Proceedings ofthel9th International Joint Conference on Artificial Intelligence(IJCAI), Edinburgh, Scotland,UK,2005:659-664.
[87]MAEDCHE A, STAAB S. Semi-automatic engineering of ontologies from text[C]. Proceedings of the 12th International Conference on Software Engineering and Knowledge Engineering. Chicago,2000.
[88]谌贻荣,陆勤,李文捷,等.中文核心领域本体构建的一种改进方法[J].中文信息学报,2010,24(1)：48-53.
[89]张新.基于中文科技论文的本体交互式构建方法研究[D].大连：大连理工大学,2006.11.
[90]陈珂.构造领域本体概念关系的自动抽取[D].上海：上海交通大学,2008.1.
[91]王磊,周宽久,仇鹏,等.领域本体自动构建研究[J].情报学报,2010,29(1)：30-33.
[92]程晓,郑德权,杨宇航,等.面向半结构化文本的领域本体关系抽取[C].第十届全国计算语言学学术会议,中国山东烟台,2009.
[93]何婷婷,张小鹏.特定领域本体自动构造方法[J].计算机工程,2007,33(22)：235-237.
[94]BERLAND M, CHARNIAK E. Finding parts in very large corporat[C]. Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, College Park, Maryland,1999:57-64.
[95]WIDDOWS D, DOROW B. A graph model for unsupervised lexical acquisition [C]. Proceedings of the 19th International Conference on Computational Linguistics, 2002:1093-1099.
[96]ETZIONI O, CAFARELLA M, DOWNEY D, et al. Unsupervised named-entity extraction from the web:an experimental study [J]. Artificial Intelligence,2005,165(1): 91-134.
[97]NAKAYA N, KURUREMATSU M, YAMAGUCHI T. A domain ontology development environment using a MRD and text corpus [C]. Proceedings of the Joint Conf. on Knowledge Based Software Engineering,2002:909-912.
[98]WordNet. http://wordnet.princeton.edu/, (Accessed on Oct 8,2006).
[99]NAVIGLI R, VELARDI P. Learning domain ontologies from document warehouses and dedicated web sites [J]. Computational Linguistics,2004,30(2):151-179.
[100]POESIO M, ALMUHAREB A. Identifying concept attributes using a classifier [C]. Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition, Ann Arbor, 2005:18-27.
[101]NANCY A. Chinehor. Overview of MUC-7/MET-2. Proceedings of the Seventh Message Understanding Conference(MUC-7). Fairfax. Virginia,1998.
[102]ACE 2004. The Automatic Content Extraction (ACE) Projects,2007. http://www.1dc.upenn.edu/Projects/ACE/.
[103]ACE 2005. The Automatic Content Extraction(ACE) Projects,2007. http://www.1dc.upenn.edu/Projects/ACE/
[104]KAMBHATLA N. 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, Barcelona, Spain,2004:178-181.
[105]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.
[106]董静,孙乐,冯元勇,等.中文实体关系抽取中的特征选择研究[J].中文信息学报,2007,21(4)：80-85.
[107]黄鑫,朱巧明,钱龙华,等.基于特征组合的中文实体关系抽取[J].微电子学与计算机,2010,27(4)：198-200.
[108]刘克彬,李芳,刘磊.基于核函数中文关系自动抽取系统的实验[J].计算机研究与发展,2007,44(8)：1406-1411.
[109]HUANG R H, SUN L, FENG Y Y. Study of kernel-based methods for Chinese relation extraction [J]. Information retrieval technology,2008,4993:598-604.
[110]ZHOU G D, QIAN L H, FAN J X. Tree kernel-based semantic relation extraction with rich syntactic and semantic information[J]. Information Science,2010,180(8): 1313-1325.
[111]虞欢欢,钱龙华,周国栋,等.基于合一句法和实体语义树的中文语义关系抽取[J].中文信息学报,2010,24(5)：17-23.
[112]POESIO M, BARBU E, GIULIANO C, et al. Supervised relation extraction for ontology learning from text based on a cognitively plausible model of relations [C]. Proceedings of the Italy 3rd Workshop on Ontology Learning and Population, Patras,2008:1-5.
[113]FERNANDEZ-LOPEZ M. Overview of methodologies for building ontologies[C]. Proceedings of the IJCA1-99 Workshop on Ontologies and Problem-Solving Methods (KRE5) Stockholm, Sweden,1999.8.
[114]GRUNINGER M, FOX M S. Methodology for the design and evaluation of ontologies [C]. Workshop on Basic Ontological Issues in Knowledge Sharing, IJCAI-95, Montreal,1995.
[115]BEMARAS A, LARESGOITI I, CORERA J. Building and reusing ontologies for electrical network applications [C]. Proceedings of the European Conference on Artificial Intelligence, Budapest, Hungary:John Wiley and Sons,1996:298-302.
[116]FERNANDEZ M, GOMEZ-PEREZ A, JURISTO N. Methontology:from ontological art towards ontological engineering [J]. Symposium on ontological Engineering of AAAI, Stanford (California),1997:24-26.
[117]SWARTOUT B, PATIL R, KNIGHT K, et al. Toward distributed use of large-scale ontologies [J]. Symposium on Ontological Engineering of AAAI. Stanford(California),1997:138-148.
[118]NOY N F, MCGUINNESS D L. Ontology development 101:a guide to creating your first ontology,2001-08. http://protege.stanford. edu/publications/ontology_development/ontology101.pd f, Accessed:2008.02
[119]黄伟.本体构建与语义集成研究[D].南京：东南大学计算机应用专业,2005.
[120]http://www.idef.com/idef5.html [EB/OL]. Accessed:2008.02
[121]HORRIDGE M, KNUBLAUCH H, RECTOR A, er al. A practical guide to building OWL ontology using the protege-OWL plugin and CO-ODE tools [EB]. The University of Manchester, 2004.8.
[122]SMITH M K, WELTY C, MCGUINNESS D L. OWL web ontology language guide [EB/OL]. http://www.w3.org/TR/2004/REC-owl-guide-20040210/, (Accessed on Jan.1,2005). Germany, August 15th,2000.
[123]BECHHOFER S, HARMELEN F V, HENDLER J. OWL web ontology language reference [EB/OL]. http://ww.w3.org/TR/2004/REC-owl-ref-20040210/, (Accessed on Jan.1,2005).
[124]OMELAYENKO B. Learning of ontologies for the web:the analysis of existent approaches[C]. Proceedings of the International Workshop on Web Dynamics, London, 2001.
[125]HUANG D G, TONG D Q, LUO Y Y. HMM revises low marginal probability by CRF for Chinese word segmentation [C]. Proceedings of CIPS-SIGHAN Joint Conference on Chinese Processing, Beijing,2010:216-220.
[126]FRAKES W B, BAEZA-YATES R. Information Retrieval Data Structures & Algorithms[M]. Prentice Hall PTR,1992:66-82.
[127]仁禾,曾隽芳.一种基于信息熵的中文高频词抽取算法[J].中文信息学报.2006,20(5): 40-43.
[128]LAFFERTY J, MCCALLUM A, PEREIRA F. Conditional random fields:probabilistic models for segmenting and labeling sequence data [C]. Proceedings of the International Conference on Machine Learning, Williams,2001:282-289.
[129]聂规划,傅魁.基于Web的中文本体学习研究[J].情报杂志,2008,6：13-16.
[130]LEWIS D D, GALE W A. A sequential algorithm for training text classifiers[C]. Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,1994:3-12.
[131]LTP platform, http://ir.hit.edu.cn/ltp/.
[132]MCDONALD R, PEREIRA F, RIBAROV K, et al. Non-projective dependency parsing using spanning tree algorithms [C]. Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Vancouver,2005:523-530.
[133]COLLINS M, DUFFY N. Convolution kernels for natural language [C]. Proceedings of the NIPS'2001,2001:625-632.
[134]ZHANG M, ZHANG J, SU J, et al. 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.
[135]MICHAEL W. An introduction to multi agent systems [M]. Chichester, England:John Wiley & Sons,2002.
[136]LIAO LJ, XU KQ, LIAO SS. Constructing intelligent and open mobile commerce using a semantic web approach [J]. Journal of Information Science,2005,31(5): 407-419.
[137]STONE P, VELOSO M. Multiagent systems:a survey from a machine learning perspective [J]. Autonomous Robots,2000,8(3):345-383.
[138]PRODROMIDIS A L, CHAN P K, STOLFO S J. Meta-learning in distributed data mining systems:issues and approaches [C]. Proceedings of Distributed Data Mining, Menlo Park, California, USA,2000:81-114.
[139]ZHANG J, OUYANG Y, LI W J, et al. A novel composite kernel approach to Chinese entity relation extraction [C]. Proceedings of the ICCPOL 2009,2009,5459: 236-247.
[140]中华人民共和国国务院.国函(1987)142号,1987.8.12.
[141]于娟,马金平,李永.基于Web本体语言OWL的知识表示[J].计算机工程与设计,2006,27(22)：4256-4357.
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