摘要
Web向社会化与语义化的不断演进和信息资源组织理论的不断革新共同促生了folksonomy并推动其不断发展，而folksonomy的不断优化又离不开folksonomy知识发现理论的支撑，同时形式概念分析、本体等理论的发展又为folksonomy知识发现注入了新的活力，一种基于形式概念分析的folksonomy知识发现理论呼之欲出！
回顾当前folksonomy知识发现理论的优势劣态，虽在folksonomy知识发现的各个主要方向取得了一些散落的成果，体现于folksonomy用户行为、folksonomy用户偏好和folksonomy语义关系等方面，但仍未建立完善的将三者统一到一套完整框架下的Folksonomy知识发现理论体系，更缺少对对Folksonomy知识发现的基本原理、基本方向、目标产物、技术工具和详细流程等全面介绍与阐述。
基于形式概念分析的folksonomy知识发现理论为弥补上述缺陷提供了可能。在确立folksonomy是数据，形式概念分析是工具，知识发现是目标的角色定位后，基于形式概念分析的folksonomy知识发现螺旋演进模型应运而生，其高度概括了基于形式概念分析的folksonomy知识发现的组成要素、角色要素、功能要素及各个要素之间的紧密关系，并将基于形式概念分析的folksonomy知识发现过程归纳为问题定义、数据获取、数据准备、数据组织、数据挖掘、知识生成和评估反馈七个阶段。另外，在用户需求和数据组织的一唱一和下，基于形式概念分析的folksonomy知识发现核心方向的歧化也通过folksonomy多值形式背景的选择得以实现，folksonomy的用户行为背景、用户偏好背景和语义关系背景分别决定了基于形式概念分析的folksonomy知识发现的三大方向。
基于形式概念分析的folksonomy用户行为分析以单用户的用户标记行为、用户群的聚集与形成、用户群标记行为和用户群的遴选为知识发现目标，通过基于形式概念分析的folksonomy用户行为分析模型，先利用folksonomy数据集构建相应的folksonomy用户行为形式背景——“用户-标签”形式背景FU:= (U,T×R,YU)并将其转换为folksonomy用户行为概念格，并在folksonomy用户行为概念格分析的基础上使用回溯法获取folksonomy单用户用户标记行为链，使用用户群层级结构映射规则获取folksonomy用户群层次树，使用FREtirj计算公式获取folksonomy用户群标记行为频率，使用folksonomy用户群遴选的规则遴选出folksonomy典型用户群。folksonomy用户行为分析为folksonomy用户偏好挖掘提供活跃用户、典型用户群及folksonomy用户标记行为频率等参数，为folksonomy语义关系发现提供低频标签过滤、稳态folksonomy系统判断及语义浮出判断的依据。
基于形式概念分析的folksonomy用户偏好挖掘以folksonomy用户偏好树的构建为目标，通过基于形式概念分析的folksonomy用户偏好挖掘模型，分别从活跃单用户和典型用户群的用户偏好各自的数据集出发构建相应的folksonomy用户偏好形式背景——“资源-用户”形式背景FR:= (R,U×T,Y~R)，并分别将单用户folksonomy用户偏好形式背景和用户群folksonomy用户偏好形式背景转化为各自的用户偏好概念格。通过在概念格基础上识别用户偏好，并借鉴TF/IDF原理充分考虑了用户偏好的“频率”和“普遍重要性”两项因素，分别提出了folksonomy单用户偏好权重计算公式和folksonomy用户群偏好权重计算公式，进而分别利用相应的用户偏好权重计算公式（PW公式）和用户偏好相似度计算公式（PS公式）遴选单用户的用户偏好和用户群的用户偏好，最终构建folksonomy用户偏好树来表示folksonomy用户偏好知识。
基于形式概念分析的folksonomy语义关系发现以发现folksonomy中的隐含语义为目标，确立了本体在folksonomy语义关系表示中的重要作用，通过基于形式概念分析的folksonomy语义关系发现模型，从“语义浮出”的稳态folksonomy的数据集出发构建相应的folksonomy语义关系形式背景——“资源-标签”形式背景FR:= (R,T×U,Y~R)，并将之转化为相应的概念格。在folksonomy语义关系概念格基础上，利用folksonomy语义关系概念格向folksonomy局部本体的映射规则，得出相应的folksonomy局部本体模型，之后选用合适的本体编辑工具（如protégé等）和本体描述语言（如owl语言）对folksonomy局部本体模型进行形式化，最终得到一个形式化的揭示folksonomy各种隐含语义关系的folksonomy局部本体。
基于形式概念分析的folksonomy知识发现的理论体系充分利用了folksonomy多值形式背景的不同形式适宜地表示了folksonomy知识发现的三大核心方向，并针对不同类型的概念格分别提出了相应的模式识别和知识解释方式，从而依托概念格实现了folksonomy知识发现。另外，基于形式概念分析的folksonomy知识发现的螺旋演进思想也为获取用户满意的folksonomy知识提供了保障。经过在Delicious系统数据的测试，基于形式概念分析的folksonomy知识发现理论具有创新性、科学性、合理性和操作性。
基于形式概念分析的folksonomy知识发现理论不仅深化了folksonomy理论研究，也拓展了知识发现理论的内涵和外延，最重要的是揭示了基于形式概念分析的folksonomy知识发现的客观规律。该理论必将提高folksonomy系统中知识发现的效率和能力，进而带来folksonomy自身的不断优化，最终促进web2.0下folksonomy的不断发展和广泛应用。因此，无论是理论上还是实践上，基于形式概念分析的folksonomy知识发现理论都具深远意义！
Both the constantly evolving of web to socialization and semantization andthe constant innovation of information resources organization theory not onlyfacilitate the emergence of folksonomy but also promote its continuousdevelopment. The optimization of folksonomy can not be separated from thesupport of folksonomy knowledge discovery, while formal concept analysis,ontology and other related theory has injected new vitality for folksonomyknowledge discovery, A FCA-based folksonomy knowledge discovery theoryget ready to come out!
when reviewing the advantages and disadvantages of knowledgediscovery theory in folksonomy，it can be found that some scattered results inthis area has been achieved, including user behaviors, user preferences andsemantic relations in folksonomy, but a prefect knowledge discoverytheoretical system which integrate the three directions into a completeframework has not yet been established in folksonomy, along with the lack of acomprehensive introduction to the basic principles, basic direction, goals andproducts, technology and tools, detailed operational processes of knowledgediscovery in folksonomy.
The FCA-based folksonomy knowledge discovery theory offers thepossibility to compensate for these shortcomings. After the respective role offolksonomy, FCA and knowledge discovery has been determined, AFCA-based folksonomy knowledge discovery spiral evolution model came intobeing. the model highly summarizes the constituent elements，role elements，functional elements and the tight relationship between these various elementsin FCA-based folksonomy knowledge discovery，and divides the wholeprocess into seven stages which are orderly composed of problem definition,data acquisition, data preparation, data organization, data mining, knowledge generation and evaluation&feedback. In addition, with the user needs and dataorganization echoing each other, the disproportionation of the core direction inFCA-based folksonomy knowledge discovery is implemented by choosingappropriate multi-value context of folksonomy. In deed, the user behaviorcontext, the user preferences context and the semantic relations contextrespectively decide three major directions of FCA-based folksonomyknowledge discovery.
FCA-based folksonomy user behavior analysis takes tagging behavior ofsingle-user, gathering and formation of user group, tagging behavior of usergroup and typical user group selection as the targets. With the supporting ofFCA-based folksonomy user behavior analysis model, A folksonomy userbehavior formal context called“user-tag”context which denoted as FU:=(U,T×R, Y~U) is constructed from folsonomy data sets, and then the context isconverted to the folksonomy user behavior concept lattice. on the basis ofanalyzing the user behavior concept lattice, the single-user tagging behaviorchain can be obtained by retrospective method, the user groups hierarchy treecan be got by user group hierarchy mapping rules, the frequency of taggingbehavior of user groups can be calculated by the FREtirj formula, also thetypical user group can be obtained by selection rules. FCA-based folksonomyuser behavior analysis not only provides the parameters just as activesingle-user, typical user groups and the frequency of tagging behavior forfolksonomy user preferences, but also provides the basis for low-frequency tagfiltering, steady-state folksonomy system judgment and semantics emergingjudgment in folksonomy semantic relations discovery.
FCA-based folksonomy user preferences mining take the construction ofuser preferences tree as the target. under the supporting of FCA-basedfolksonomy user preferences mining model,and beginning with respective datasets of active single-user and typical user group, folksonomy userpreferences formal context called“resource-user”context which denoted asFR:= (R,U×T, Y~R) is constructed, and then the context for single-user and user group is converted to the folksonomy for each other. Then the user preferenceweights formula for the two is proposed by identifying the user preferences onthe user preferences concept lattice and considering the factors of“frequence”and“universal importance”which learned from the TF/IDF principle. Finally,the folksonomy user preferences tree is build for user preferences expressionthrough sorting user preferences according to user preferences weight formulaand user preferences similarity formula.
FCA-based folksonomy semantic relations discovery takes the impliedsemantic in folksonomy as the target and recognize the important role ofontology in the expression of folksonomy semantic relationship. Through thesupporting of FCA-based folksonomy semantic relations discovery model, Afolksonomy semantic relations formal context called“resource-tag”contextwhich denoted as FU:= (U,T×R, Y~U) is constructed from a“steady–state”and“semantics emerging”folsonomy data sets and then converted to thefolksonomy semantic relations concept lattice. Using mapping rules fromfolksonomy semantic concept lattice to the local ontology, the local ontologymodel is constructed on the basis of analyzing the folksonomy semanticconcept lattice. Ultimately, the formal local ontology for revealing variety ofimplicit semantic relations in folksonomy is achieved by choosing appropriateontology editing tools (such as the protégé) and ontology description language(such as the owl language) to formalize the local ontology model.
The FCA-based folksonomy knowledge discovery theoretical system is anew and innovative theory that makes full use of the different forms of thefolksonomy multi-value contexts which befittingly show the three core directionof folksonomy knowledge discovery, and propose corresponding patternrecognition and knowledge explain methods for different types of conceptlattice, thus realize knowledge discovery relyingon the concept lattice. Inaddition, the idea of spiral evolution in FCA-based folksonomy knowledgediscovery process also provided a guarantee to obtain user-satisfiedfolksonomy knowledge. Finally, we can get the conclusion that the new FCA-based folksonomy knowledge discovery theory is scientific, reasonableand operational after the data test using dataes from Delicious web site.
The FCA-based folksonomy knowledge discovery theory not onlydeepens the folksonomy theoretical study, but also extends the intent andextent of the knowledge discovery theory, most importantly, it reveal theobjective laws of FCA-based folksonomy knowledge discovery.the theory willimprove the efficiency and capacity of knowledge discovery in folksonomysystems, promote folksonomy constantly self-optimization, and ultimatelyboost the continuous development and wide application of folksonomy inweb2.0 environment. Therefore, both in theory and in practice, FCA-basedfolksonomy knowledge discovery theory has far-reaching significance!
引文
[1] Tim O'REILLY.What Is Web 2.0: Design Patterns and Business Models for the NextGeneration of Software[J].Design.2007,65(65):17-37
[2] Smith, G. Folksonomy: Socialclassification[EB/OL].(2004)[2010-10-13]http://atomiq.org/archives/2004/08/folksonomy_social_classification.html
[3] Mathes, A. Folksonomies: cooperative classification and communication through sharedmetadata[EB/OL].(2004)[2010-10-14]http://www.adammathes.com/academic/computer-mediated-communication/folksonomies.html
[4] Clay Shirky .folksonomies + controlled vocabularies[EB/OL].(2005)[2010-10-15]http://many.corante.com/archives/2005/01/07/folksonomies_controlled_vocabularies.php
[5] David Weniberger.Taxonomies to Tags: From Trees to Piles of Leaves[EB/OL].(2005)[2010-10-15] http://cdn.oreilly.com/radar/r1/02-05.pdf
[6] Louis Rosenfeld. Folksonomies? How about Metadata Ecologies? [EB/OL].(2005-06)[2010-10-15] http://louisrosenfeld.com/home/bloug_archive/000330.html
[7] Emanuele Quintarelli. Folksonomies: power to the people[EB/OL].(2005-07-24)[2010-10-16] http://www.iskoi.org/doc/folksonomies.htm
[8] B.甘特尔,R.威尔著,马垣,张学东等译.形式概念分析[M].北京:科学出版社.2007:15-46
[9] Marinho Leandro Balby, Buza Krisztian, Schmidt-Thieme Lars. Folksonomy-BasedCollabulary Learning[C]. ISWC '08 Proceedings of the 7th International Conference on TheSemantic Web.Berlin.Springer.2008,5318:261-276
[10] Chung MinGyo, Wang Taehyung (George), Sheu Phillip C.-Y.Video summarisation basedon collaborative temporal tags[J]. Online Information Review.2011,35(4):653-668
[11] Kim Hyun Hee.Toward Video Semantic Search Based on a Structured Folksonomy[J].Journal of the american society for information science and technology .2011,62(3):478-492
[12] Pi Shih-Ming, Liao Hsiu-Li, Liu Su-Houn,et al. Framework for Classifying Website ContentBased on Folksonomy in Social Bookmarking[J].Intelligent computing and informationscience.2011,135:250-255
[13] Wu Jianlin, Yan Guocong.A new approach to implement enterprise content managementsystem using RSS and Folksonomy[C] International Conference on Research and PracticalIssues of Enterprise Information Systems (CONFENIS 2007).Berlin.Springer-Verlag.2007:1101-1110
[14] Pittarello Fabio. Semantic description of web 3D worlds through socialtagging[J].International journal of software engineering and knowledgeengineering.2011.21(1):73-102
[15] Gasevic Dragan. Zouaq Amal, Torniai Carlo.et al.An Approach to Folksonomy-BasedOntology Maintenance for Learning Environments[J].IEEE transactions on learningtechnologies.2011(4):301-314
[16] Kim Heung-Nam, Rawashdeh Majdi, Alghamdi Abdullah.Folksonomy-based personalizedsearch and ranking in social media services[J].Information Systems.2012.37(1):61-76
[17] Cantador Ivan, Konstas Ioannis, Jose Joemon M.Categorising social tags to improvefolksonomy-based recommendations[J].Journal of web semantics.2011,9(1):1-15
[18] Montanes Elena,Ramon Quevedo Jose, Diaz Irene.et al.TagRanker: learning to recommendranked tags[J]. Logic Journal of the IGPL.2011,19(2):395-404
[19] Jin Yan'an, Li Ruixuan, Wen Kunmei.et al.Topic-based ranking in Folksonomy viaprobabilistic model[J]. Artificial Intelligence Review.2011,32(6):139-151
[20] Mesnage Cedric, Carman Mark.Tag Navigation[C].2nd International Workshop on SocialSoftware Engineering and Applications. New York.ACM.2009:29-32
[21] Sommaruga Lorenzo, Rota Petra, Catenazzi Nadia.Tagsonomy: Easy Access to Web Sitesthrough a Combination of Taxonomy and Folksonomy[C].In 7th Atlantic Web IntelligenceConference.Berlin.Springer-Verlag.2011,86:61-71
[22] Kiu Ching-Chieh, Tsui Eric.TaxoFolk: A hybrid taxonomy-folksonomy structure forknowledge classification and navigation[J].Expert Systems withApplications.2011.38(5):6049-6058
[23] Geldart Joe, Cummins Stephen.The Automatic Integration of Folksonomies withTaxonomies Using Non-axiomatic Logic[M]. In Information Systems DevelopmentTowards a Service Provision Society. Berlin:Springer-Verlag. 2009:365-372
[24] Sarah Hayman, Nick Lothian. Taxonomy directed folksonomies: Integrating user taggingand controlled vocabularies for Australian education networks[J].Africa,2007(8):1-27
[25] Nocera Antonino, Ursino Domenico. An approach to providing a user of a socialfolksonomy with recommendations of similar users and potentially interesting resources[J].Knowledge-based Systems.2011,24(8):1277-1296
[26] Zhang Zi-Ke, Zhou Tao, Zhang Yi-Cheng.Personalized recommendation via integrateddiffusion on user-item-tag tripartite graphs[J].Physica A: Statistical Mechanics and itsApplications.2010,389(1):179-186
[27] Ilhan Nagehan, Oegueduecue Sule Guenduez. A recommender model for socialbookmarking sites[C].15th international conference on soft computing, computing withwords and perceptions in system analysis, decision and control.出版地不详.出版者不详.2010:136-139
[28] Ju Sanghun, Lee Sangjun, Hwang Kyu-Baek.Applying Machine Learning Techniques toTag Recommendation in Social Bookmarking Systems[J]. INFORMATION-ANINTERNATIONAL INTERDISCIPLINARY JOURNA.2010.13(5):1613-1624
[29] Lohmann Steffen, Ziegler Juergen, Tetzlaff Lena. Comparison of Tag Cloud Layouts:Task-Related Performance and Visual Exploration[C]. INTERACT '09 Proceedings ofthe 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part I.Berlin.Springer-Verlag.2009,5726:392-404
[30] Schrammel Johann, Leitner Michael, Tscheligi Manfred.Semantically Structured TagClouds: An Empirical Evaluation of Clustered Presentation Approaches[C].27th AnnualCHI Conference on Human Factors in Computing Systems. NewYork.ACM.2009:2037-2040
[31] Schrammel Johann, Deutsch Stephanie, Tscheligi Manfred.Visual Search Strategies of TagClouds - Results from an Eyetracking Study[C]. INTERACT '09 Proceedings of the 12thIFIP TC 13 International Conference on Human-Computer Interaction: Part IIBerlin.Springer-Verlag.2009,5727:819-831
[32]杨秀丹,苏娜.国外民俗分类发展综述[J].图书情报工作.2007(6):59-61
[33]王翠英. Folksonomy初探[J].图书馆学研究.2007(5):85-87
[34]余金香. Folksonomy及其国外研究进展[J].图书情报工作. 2007,51(7):38-40
[35]丁莉,胡石.基于自由分类法的数字资源组织探析[J].情报科学.2011(12): 1834-1837
[36]贾君枝,李婷.分众分类与书目记录结合研究[J].情报理论与实践.2011(7): 38-43
[37]贾君枝.分众分类法与受控词表的结合研究进展[J].中国图书馆学报.2010(5): 96-101
[38]徐志玮,曹文泉. Folksonomy与受控词汇在OPAC的应用研究[J].新世纪图书馆.2011(6):32-35
[39]徐少同.网络信息自组织视角下的Folksonomy优化[J].图书情报工作.2009(10):102-105
[40]王翠英.本体与Folksonomy的比较研究[J].图书馆建设.2008(5): 85-88
[41]张欢,齐向楠. Folksonomy在图书馆中的应用研究. [J].农业图书情报学刊.2011(11):62-66
[42]郑燃.基于Folksonomy的图书馆信息组织应用研究[J].图书馆杂志.2011,30(4):19-23
[43]曹淼.分众分类法在图书馆中的应用及优化[J].图书馆建设.2011(2):45-48
[44]魏来.基于概念空间模型的folksonomy标签聚类方法研究[J].情报杂志.2011(4):137-142
[45]王翠英.标签的聚类分析研究[J].现代图书情报技术.2008(5): 67-71
[46]吴江.自由分类标签类聚成网状分类结构研究与实现[J].图书情报知识,2011(1):75-81
[47]王翠英.基于Folksonomy的用户偏好研究进展[J].现代图书情报技术.2009(6):37-43
[48] Shilad Sen.Tagging,Communities,Vocabulary,Evolution[C]. CSCW '06 Proceedings of the2006 20th anniversary conference on Computer supported cooperative work. New York.ACM.2006:181-190
[49] Golder S, Huberman B. Usage patterns of collaborative tagging systems[J].Journal ofInformation Science.2006,32(2):198-208.
[50] Rashmi Sinha.A Cognitive Analysis ofTagging[EB/OL].(2005)[2011-9-3]http://blog.jackvinson.com/archives/2005/10/01/a_cognitive_analysis_of_tagging.html
[51] Benjamin Szekely, Elias Torres. Ranking Bookmarks and Bistros: Intelligent Communityand FolksonomyDevelopment[EB/OL].(2005)[2011-9-3]http://labs.rightnow.com/colloquium/papers/tagrank.pdf
[52] Ciro Cattuto, Vittorio Loreto, Luciano Pietronero.Collaborative Tagging and SemioticDynamics[EB/OL].(2007) [2011-9-3]http://arxiv.org/pdf/cs/0605015v1
[53] Ching-man Au Yeung, Nicholas Gibbins, Nigel Shadbolt. Collective User Behaviour andTag Contextualisation in Folksonomies[EB/OL].(2008)[2011-7-29].http://eprints.ecs.soton.ac.uk/16990/1/cmauyeung-CollectiveFolksonomy.pdf
[54] Philip J. Binkowski.The Effect of Social Proof on Tag Selectionin Social BookmarkingApplications[EB/OL].(2006) [2011-9-3]http://www.libsearch.com/view/595206
[55] Lyn Gattis.Planning and Information Foraging Theories and TheirValue to the NoviceTechnical Communicator[J]. ACM Journal of ComputerDocumentation.2002,26(2):168-175
[56] Dan Cosley, Shyong K. Lam, Istvan Albert.et al.Is Seeing Believing? How RecommenderInterfaces Affect Users’Opinions[C Proceedings of the SIGCHI conference on Humanfactors in computing systems. New York.ACM press.2003:585-592
[57] Marieke Guy, Emma Tonkin. Folksonomies: tidying up tags?[EB/OL].(2006)[2011-10-16].http://www.dlib.org/dlib/january06/guy/01guy.html
[58] Cameron Marlow, Mor Naaman, danah boyd.PositionPaper,Tagging,Taxonomy,Flickr,Ar-ticle,Toread[EB/OL].(2006)[2011-10-16].http://www.danah.org/papers/WWW2006.pdf
[59] David R. Millen, Jonathan Feinberg, Bernard Kerr.Dogear:Social Bookmarking in theEnterprise[C]. CHI '06 Proceedings of the SIGCHI conference on Human Factors incomputing systems. New York.ACM.2006:111-120
[60] Samuel Kai Wah Chu, Dinesh Rathi, Helen S. Du. Users’Behaviour in CollaborativeTagging Systems[EB/OL].(2010)[2011-7-29]http://www.cais-acsi.ca/proceedings/2010/CAIS093_ChuRathiDu_Final.pdf
[61] Ben Lund.Social Bookmarking Tools (II) A CaseStudy[EB/OL].(2005)[2011-10-16].http://dlib.org/dlib/april05/lund/04lund.html
[62] Umer Farooq, Thomas G. Kannampallil, Yang Song.et al. Evaluating Tagging Behavior inSocial Bookmarking Systems: Metrics and design heuristics[EB/OL].(2007)[2011-7-29]http://research.microsoft.com/pubs/78959/p351-farooq.pdf
[63] Robert Wetzker, Carsten Zimmermann, Christian Bauckhage.Analyzing SocialBookmarking Systems: A del.icio.us Cookbook[EB/OL].(2008-07)[2011-7-29]http://www.dai-labor.de/fileadmin/files/publications/wetzker_delicious_ecai2008_final.pdf
[64] Beate Krause, Christoph Schmitz, Andreas Hotho.et al. The Anti-Social Tagger-DetectingSpam in Social Bookmarking systems.[EB/OL].(2008-04)[2011-7-29]http://www.cs.xu.edu/csci390/09s/krause_2008_anti_social_tagger.pdf
[65] Koutrika Georgia, Effendi Frans, Gyongyi Zoltan.Combating Spam in Tagging Systems:AnEvaluation[EB/OL].(2007) [2011-7-29].http://ilpubs.stanford.edu:8090/816/1/2007-30.pdf
[66]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(2): 117-120
[67]苏杨,石豪,赖雯,赵英.利用同义词环改进基于folksonomy的用户分类[J].图书情报工作.2011(8): 58-61
[68]熊回香,王学东.面向Web3. 0的分众分类研究[J].图书情报工作.2010(3):104-107
[69] Hung C C, Huang Y C, Hsu J Y.et al.Tag-Based User Profiling for Social MediaRecommendation[EB/OL].(2008)[2011-7-29]http://www.aaai.org/Papers/Workshops/2008/WS-08-06/WS08-06-006.pdf
[70] Michlmayr E, Cayzer S. Learning User Profiles from Tagging Data and Leveraging Themfor Personal (ized) Information Access[EB/OL].(2007)[2011-8-20]http://www2007.org/workshops/paper_29.pdf
[71] Krishnan Ramanathan, Julien Giraudi, Ajay Gupta. Creating hierarchical user profiles usingWikipedia[EB/OL].(2008)[2011-8-20].http://www.hpl.hp.com/techreports/2008/HPL-2008-127.pdf
[72] Au Yeung C M, Gibbins N, Shadbolt N. A Study of User Profile Generation fromFolksonomies[EB/OL].(2008)[2011-8-20].http://eprints.ecs.soton.ac.uk/15222/1/swkm2008_paper.pdf
[73] Daniela Godoy, Analía Amandi. Hybrid Content and Tag-based Profiles forRecommendation in Collaborative Tagging Systems [C]. LA-WEB '08 Proceedings of the2008 Latin American Web Conference. Washington, DC.IEEE.2008:58-65.
[74] Liu H, Maes P. InterestMap: Harvesting Social Network Profiles forRecommendations[EB/OL].(2005)[2011-8-20].http://imap.larifari.org/_/writing/BP2005-InterestMap.pdf
[75] Zhang Yun, Feng Boqin. Tag-Based User Modeling Using Formal Concept Analysis [C]. InProceedings of the 8th IEEE International Conference on Computer and InformationTechnology.Washington, DC.IEEE.2008: 485-490.
[76] Kumar Harshit, Park Pil-Seong, Kim Hong-Gee.Using Folksonomy for Building UserPreference List[C]. ISPAW '11 Proceedings of the 2011 IEEE Ninth InternationalSymposium on Parallel and Distributed Processing with Applications Workshops.Washington, DC.IEEE.2011:273-271
[77] Saito Junki, Yukawa Takashi.Extracting User Interest for User Recommendation Based onFolksonomy. IEICE Transactions On Information And Systems.2011(6):1329-1332
[78] Yeung Ching-man Au, Gibbins Nicholas, Shadbolt Nigel. Multiple Interests of Users inCollaborative Tagging Systems[C].2008 IEEEWICACM International Conference on WebIntelligence and Intelligent Agent Technology.Washington, DC.IEEE.2008:115-118
[79] Martin N. Szomszor, Iván Cantador, Harith Alani. Correlating User Profiles from MultipleFolksonomies[C]. HT '08 Proceedings of the nineteenth ACM conference on Hypertext andhypermedia. New York.ACM.2008:33-42.
[80] Martin Szomszor, Harith Alani, Ivan Cantador.et al. Semantic Modelling of User InterestsBased on Cross-Folksonomy Analysis[C]. ISWC '08 Proceedings of the 7th InternationalConference on The Semantic Web.Berlin.Springer.2008:632-648.
[81]魏来.国外Folksonomy语义丰富研究综述[J].情报资料工作.2010(3):40-44
[82] Schmitz C, Hotho A, Jaschke R,et al. Mining Association Rules inFolksonomies[EB/OL].(2006)[2011-8-20].http://www.kde.cs.uni-kassel.de/hotho/pub/2006/schmitz2006asso_ifcs.pdf
[83] Zhang L, Wu X, Yu Y. Emergent Semantics from Folksonomies:A Quantitative Study[J].Journal on Data Semantics.2006 (6):168-186.
[84] Halpin H, Valentin Robu, Hana Shepherd. The complex dynamics of collaborativetagging[C].In Proceedings of the 16th international conference on World Wide Web.NewYork.ACM press.2007:211-220
[85] Sabou M, Motta E. Using the semantic web as background knowledge for ontologymapping [C]. In: The 1st International Workshop on Ontology Matching (OM-2006). TheNetherlands.IOS Press Amsterdam.2006: 56-64
[86]魏来.基于在线词表的folksonomy语义关联识别方法研究[J].情报情报工作.2011(5):103-108
[87] Laniado D, Davide Eynard, Marco Colombetti. Using WordNet to turn a folksonomy into ahierarchy of concepts[C]. In: Semantic Web Application and Perspectives - Fourth ItalianSemantic Web Workshop. Princeton. Citeseer.2007:192-201
[88] Limpens, Freddy, Fabien Gandon.et al.Bridging ontologies and folksonomies to leverageknowledge sharing on the social web: A brief survey[C]. In 2008 23rd IEEE ACMInternational Conference on Automated Software Engineering Workshops. Piscataway,N.J.IEEE press.2008:13-18
[89] Miao Chen, Xiaozhong Liu, Jian Qin. Semantic Relation Extraction fromSocially-Generated Tags:A Methodology for Metadata Generation In Proceedings of the2008 International Conference on Dublin Core and Metadata Applications (2008), pp.117-127
[90] Angeletou S, SabouM, Specia L, et al. Bridging the gap between folksonomies and thesemantic web: an experience report[EB/OL].(2007)
[2011-8-20].http://people.kmi.open.ac.uk/marta/papers/semnet2007.pdf
[91] Hend S. Al-Khalifa, Hugh C. Davis. FAsTA:A folksonomy-based automatic metadatagenerator[EB/OL](2007) http://eprints.ecs.soton.ac.uk/14186/1/EC-TEL07_v2.pdf
[92] Lucia Special, Motta E. Integrating folksonomies with the semantic web[C].In Proceedingsof the 4th European conference on The Semantic Web: Research and Applications.Berlin.Springer-Verlag.2007:624-639
[93] Passant A. Using ontologies to strengthen folksonomies and enrich information retrieval inweblogs[EB/OL].(2007)[2011-9-20]. http://www.icwsm.org/papers/paper15.html
[94] Rabeeh Abbasi, Steffen Staab, Philipp Cimiano. Organizing resources on tagging systemsusing T-ORG[EB/OL].(2007)[2011-9-20].http://www.kde.cs.uni-kassel.de/ws/eswc2007/proc/OrganizingResources.pdf
[95] Thomas Vander Wal. Folksonomy explanations.[EB/OL].(2005)[2010-10-13].http://www.vanderwal.net/random/entrysel.php?blog=1622.
[96] Kim Hai-Lae, Simon Scerri, John G Breslin. et al. The state of the art in tag ontologies: Asemantic model for tagging and folksonomies [J]. Engineering.2008:128-137
[97] Newman, Richard. Tag ontology design[EB/OL]. (2005)[2011-5-20].http://www.holygoat.co.uk/projects/tags/.
[98] Gruber, Thomas. Ontology of folksonomy: A mash-up of apples and oranges[J]International Journal On Semantic Web and Information Systems.2007,3(2):1–11
[99] Knerr, Torben. Tagging ontology-Towards a common ontology forfolksonomies[EB/OL](2006)[2011-5-12].http://tagont.googlecode.com/files/TagOntPaper.pdf
[100] SCOT[EB/OL]. [2010-10-16]http://scot-project.org/
[101] MOAT[EB/OL]. [2010-10-16] http://moat-project.org/
[102]吴芬.国外标签本体研究进展[J].现代情报.2009,29(11):16-20
[103]熊回香,廖作芳.本体在Folksonomy中的应用研究[J].情报科学.2010,28(2):274-278
[104] Echarte F, Astrain J. J, Córdoba A. et al. Ontology of folksonomy: a new Modelingmethod[C/OL]. Proceedings of Semantic Authoring, Annotation and Knowledge Markup(SAAKM), Tilburg.CEUR-WS.org. (2007)[2011-04-18].http://www.gsd.unavarra.es/gsd/files/condep/EcAsCoVisaakm07f.pdf
[105] Lux M, Dsinger G. From folksonomies to ontologies: Employing wisdom of the crowds toserve learning purposes[J].International Journal of Knowledge andLearning.2007(3):515-528.
[106]何继媛,窦永香,刘东苏.大众标注系统中基于本体的语义检索研究综述[J].现代图书情报技术.2011(3):51-55
[107] Damme C. V, Hepp M, Siorpaes K. FolksOntology: An Integrated Approachfor TurningFolksonomies into Ontologies[C]. In Bridging the Gep between Semantic Web and Web 2.0SemNet 2007出版地不详.出版者不详.2007:57-70
[108] Mika P. Ontologies are us:A unified model of social networks and semantics[J].WebSemantics: Science, Services and Agents on the World Wide Web. 2007(3):5-15.
[109]张有志,王军.基于Folksonomy的本体构建探索[J].图书情报工作2008,52(12):122-125
[110]唐晓波,全莉莉.基于分众分类的本体构建分析[J].情报理论与实践.2008,31(6):931-936
[111] Lin Huairen, Davis Joseph.Computational and Crowdsourcing Methods for ExtractingOntological Structure from Folksonomy[C].In 7th Extended Semantic Web Conference(ESWC2010).Berlin.Springer.2010,6089:472-477
[112] Lin Huairen, Davis Joseph,Zhou Ying.An Integrated Approach to Extracting OntologicalStructures from Folksonomies[C].In 6th European Sematic Web Conference.Berlin.Springer.2009,5554:654-668
[113] Tang Jie,Leung H-f,Luo Q.et al.Towards Ontology Learning from Folksonomies[C].21stInternational Joint Conference on Artificial Intelligence (IJCAI-09) San Francisco, MorganKaufmann Publishers Inc. 2009:2089-2094
[114] Kim H-L, Hwang S-H, Kim H-G. FCA-based approach for mining contextualizedfolksonomy. In Proceedings of SAC'2007. Seoul.ACM press.2007:1340-1345
[115] Kang Y-K, Hwang S-H, Yang K-M.FCA-based conceptual knowledge discovery inFolksonomy[J].World Academy of Science, Engineering and Technology.2009,53:842-846
[116] Andreas Hotho, Robert J¨aschke, Christoph Schmitz.et al.Information retrieval infolksonomies: Search and ranking[C].In Proceedings of the 3rd European Semantic WebConference. Budva. Springer-Verlag.2006:411–426
[117] Andreas Hotho, Robert J¨aschke, Christoph Schmitz.et al.Trend detection infolksonomies[C]. In Proc.First International Conference on Semantics And Digital MediaTechnology (SAMT), Heidelberg, Springer-Verlag.2006,4306:56–70
[118] Robert J¨aschke,Andreas Hotho, Christoph Schmitz.Discovering shared conceptualizationsin folksonomies[J].Web Semantics: Science, Services and Agents on the World WideWeb.2008,6(1):38-53
[119] Suk-Hyung Hwang .A Triadic Approach of Hierarchical Classes Analysis on FolksonomyMining[J/OL]International Journal of Computer Science and NetworkSecurity.2007,7(8)[2011-05-25]http://www.citeulike.org/user/jychen/article/4172639
[120] David, N. S.Communal categorization:The folksonomy[EB/OL].(2004-2-16)[2010-10-13]http://davidsturtz.com/drexel/622/sturtz-folksonomy.pdf
[121]阮一峰.公众分类法（Folksonomy）[EB/OL].(2006-9-6)[2011-11-20]http://www.ruanyifeng.com/blog/2006/09/folksonomy.html
[122]维基百科分众分类法[EB/OL].[2011-11-20]http://zh.wikipedia.org/wiki/分众分类法
[123]吴开华,戴璟.公众分类法（Folksonomy）:一种新的网络信息分类方法[EB/OL].[2011-11-20]http://202.96.31.19:8080/WEB_GT/Resource/p349.ppt
[124] Hammond T, Hannay T, Lund B, et al.Social bookmarking tool(1)-A general review[J/OL].D-LibMagazine,2005,11(4).[2010-10-14]http://www.dlib.org/dlib/april05/hammond/04hammond.html
[125] Peter Merholz. metadata for the masses[EB/OL].（2004-10-19）[2010-10-14]http://www.citeulike.org/user/ket/article/1362029
[126]卜小蝶.Folksonomy的發展與應用.[J]國立成功大學圖書館館刊.2007(16):1-7
[127] folksonomy百度百科[EB/OL].[2011-10-17]http://baike.baidu.com/view/1081510.htm
[128] Delicious[EB/OL].[2011-10-17]http://www.delicious.com/
[129] Flickr[EB/OL]. [2010-10-17]http://www.flickr.com/
[130]張淇龍,卜小蝶.淺談Web 2.0與通俗分類於圖書資訊服務之應用[J].圖書與資訊學刊,2006,57:74-93
[131] Overview on ConExp [EB/OL].[2011-12-20].http://conexp.sourceforge.net/users/index.html
[132] Y.K.Kang, S.H. Hwang, et al. Development of a FCA Tool for Building ConceptualHierarchy of Clinical Data[J].Journal of the Korean Society of MedicalInformatics.2005,11(2):71-76.
[133] ToscanaJ [EB/OL].[2011-12-20].http://toscanaj.sourceforge.net/
[134] Lattice Miner [EB/OL].[2011-12-20].http://lattice-miner.sourceforge.net/
[135] Galicia Lattice Builder Home Page[EB/OL].[2011-12-20].http://www.iro.umontreal.ca/~galicia/
[136]滕广青,毕强.概念格构建工具ConExp与Lattice Miner的比较研究[J].现代图书情报技术. 2010,26 (10):17-22
[137] Lattice Miner维基百科[EB/OL].[2011-12-20].http://en.wikipedia.org/wiki/Lattice_Miner
[138] Formal Concept Analysis Homepage[EB/OL].[2011-12-20].http://www.upriss.org.uk/fca/fcasoftware.html
[139] Camelis [EB/OL].[2011-12-20].http://www.irisa.fr/LIS/ferre/camelis/
[140] Conexp-clj [EB/OL].[2011-12-20].http://daniel.kxpq.de/math/conexp-clj/
[141] FCA algorithms [EB/OL].[2011-12-20].http://fcalgs.sourceforge.net/
[142] Lattice Navigator[EB/OL].[2011-12-20].http://www.fca.radvansky.net/news.php
[143] OpenFCA[EB/OL].[2011-12-20].http://code.google.com/p/openfca/
[144] Usama M F, Gregory P, Padhraic S, et al. Knowledge Discovery and Data Mining: Towardsa Unifying Framework.[C] Proc 2nd Int Conf on Knowledge Discovery and Data MiningPortland OR. Menlo Park: AAAI Press,1996:82-88.
[145]白石磊,毛雪岷,王儒敬等基于数据库和知识库的知识发现研究综述[J].广西师范大学学报(自然科学版) 2003,21(1):136-141
[146] Usama M F, Gregory P, Padhraic S. From data mining to knowledge discovery indatabases[J].AI Magazine.1996,17(3):37-54.
[147]王敏,张志.知识发现研究文献定量分析[J].图书情报工作.2008(4):29-31
[148]王敏,张志.图书情报领域知识发现研究文献内容分析[J].现代图书情报技术.2008(2)64-68
[149]郭崇慧.数据挖掘与知识发现[EB/OL][2012-2-18]http://blog.sciencenet.cn/home.php?mod=space&uid=34250&do=blog&id=212468
[150]张玉峰.智能信息系统[M].武汉:武汉大学出版社.2008
[151]王永庆.人工智能原理与方法[M].西安:西安交通大学出版社.1998
[152]化柏林.国内外知识抽取研究进展综述[J].情报杂志.2008(2):60-62
[153] Han J, Hu X H, Cercone N. A Visualization Model of Interactive Knowledge DiscoverySystems and Its Implementations[J].Information Visualization.2003(2):105-125
[154]孙吉红,焦玉英.知识发现及其发展趋势研究[J].情报理论与实践.2006.29(5):528-530
[155] Goil S, Choudhary A. High Performance OLAP and Data Mining on Parallel Computers[J].Data Mining and Knowledge Discovery.1997(1):391-417
[156] Dozier C, Jackson P, Guo X, et al. Creation of an Expert Witness Database through TextMining[C]. In ICAIL '03: Proceedings of the 9th international conference on Artificialintelligence and law. New York.2003: 177-184
[157] Schuster A, Wolff R. Communication Efficient Distributed Mining of AssociationRules[J].Data Mining and Knowledge Discovery.2004,8(2):171-196,
[158] Neches R, Fikes R E, Gruber TR, et al. Enabling Technology for Knowledge Sharing[J].AIMagazine.1991,12(3):36-56
[159] Gruber TR.A Translation Approach to Portable Ontology Specification[J]. KnowledgeAcquisition.1993(5):199-220
[160] W. N. Borst. Construction of Engineering Ontologies for Knowledge Sharing and Reuse.[D].Enschede:PhD thesis, University of Twente, 1997
[161] Fensel D. The semantic web and its languages[J].IEEE Computer Society.2000,15(6):67-73
[162]宋炜等.语义网简明教程[M].北京:高等教育出版社.2004:117-118
[163] AG Perez, V. R. Benjamins. Overview of Knowledge Sharing and Reuse Components:Ontologies and Problem SolvingMethods[C/OL].(1999)[2011-10-5]http://oa.upm.es/6468/1/Overview_of_Knowledge.pdf
[164]张云中.基于形式概念分析的领域本体构建方法研究[D].长春:吉林大学硕士论文.2009
[165] Grigoris Antoniou, Frank van Harmelen. Web Ontology Language:OWL[C/OL]. (2004)[2011-10-5]http://www.cs.vu.nl/~frankh/postscript/OntoHandbook03OWL.pdf
[166]朱红蕾,徐志刚,李明.概念格的知识发现研究[J].微计算机信息.2006(2-3):247-249
[167]齐红.基于形式概念分析的知识发现方法研究[D].长春:吉林大学博士论文.2005
[168]唐志军.基于分布式概念格的知识发现研究[D].安徽:合肥工业大学硕士论文.2005
[169]王月行,马垣,胡志宇.基于概念格的关联规则挖掘方法.[J].计算机工程与设计.2009(22):5062-5064
[170]郭显娥,王俊红.多维概念格与关联规则发现.[J].计算机应用.2010(4): 1072-1075
[171]滕广,毕强.基于概念格的数字图书馆用户用法细分——数字图书馆用户使用方法的关联规则挖掘[J].现代图书情报技术.2010.26(3):8-12
[172]陈湘,吴跃.基于约简概念格的关联规则提取改进算法[J].计算机应用研究.2011(4):1293-1295
[173] TF-IDF原理[EB/OL][2012-2-18].http://baike.baidu.com/view/1228847.html
[174]岳爱华,孙艳妹. Taxonomy、Folksonomy和Ontology的分类理论及相互关系[J].图书馆杂志.2008,27(11):21-24
[175]李镜镜.基于标签的网络Folksonomy研究[J].科技情报开发与经济.2009,19(31):71-74
[176] Katrin Weller. Folksonomies and ontologies:two new players in indexing and knowledgerepresentation. [EB/OL].(2007)[2011-4-13]http://www.phil-fak.uni-duesseldorf.de/fileadmin/Redaktion/Institute/Informationswissenschaft/weller/1197280560weller009p.pdf
[177] Atefeh Sharif.Combining ontology and folksonomy:An Integrated Approach to KnowledgeRepresentation.[EB/OL].(2009)[2011-4-13]http://www.ifla2009satelliteflorence.it/meeting3/program/assets/AtefehSharif.pdf
[178]张云中.本体与自由分类法的融合机理研究[J].情报理论与实践. 2012.35(2):35-40
[179]刘炜.数图研究笔记: Folksonomy、Taxonomy与Ontology[EB/OL]. (2006)[2011-3-13]http://www.kevenlw.name/archives/133
[180] Shirky C. Ontology is Overrated: Categories, Links, and Tags. [EB/OL].(发表日期不详)[2010-08-28].http://www.shirky.com/writings/ontology_overrated.html
[181] Delicious开源数据片段.[EB/OL][2012-2-23]http://www.delicious.com/stacks/view/LPCRfg