Research On Knowledge Discovery Based On Formal Concept Analysis In Folksonomy

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!...