By analyzing the WWW Cache model, we bring forward a user-interest description method based on the fuzzy theory and user-interest inferential relations based on BP（baek propagation） neural network. By this method, t...By analyzing the WWW Cache model, we bring forward a user-interest description method based on the fuzzy theory and user-interest inferential relations based on BP（baek propagation） neural network. By this method, the users＇ interest in the WWW cache can be described and the neural network of users＇ interest can be constructed by positive spread of interest and the negative spread of errors. This neural network can infer the users＇ interest. This model is not the simple extension of the simple interest model, but the round improvement of the model and its related algorithm.展开更多
Fuzzy ontologics are efficient tools to handle fuzzy and uncertain knowledge on the semantic web; but there are heterogeneity problems when gaining interoperability among different fuzzy ontologies. This paper uses co...Fuzzy ontologics are efficient tools to handle fuzzy and uncertain knowledge on the semantic web; but there are heterogeneity problems when gaining interoperability among different fuzzy ontologies. This paper uses concept approximation between fuzzy ontologies based on instances to solve the heterogeneity problems. It firstly proposes an instance selection technology based on instance clustering and weighting to unify the fuzzy interpretation of different ontologies and reduce the number of instances to increase the efficiency. Then the paper resolves the problem of computing the approximations of concepts into the problem of computing the least upper approximations of atom concepts. It optimizes the search strategies by extending atom concept sets and defining the least upper bounds of concepts to reduce the searching space of the problem. An efficient algorithm for searching the least upper bounds of concept is given.展开更多
0 Introduction The surprising growth of the Internet, coupled with the rapid development of Web technique and more and more emergence of web information system and application, is bring great opportunities and big cha...0 Introduction The surprising growth of the Internet, coupled with the rapid development of Web technique and more and more emergence of web information system and application, is bring great opportunities and big challenges to us. Since the Web provides cross-platform universal access to resources for the massive user population, even greater demand is requested to manage data and services effectively.展开更多
This paper proposes a checking method based on mutual instances and discusses three key problems in the method： how to deal with mistakes in the mutual instances and how to deal with too many or too few mutual instan...This paper proposes a checking method based on mutual instances and discusses three key problems in the method： how to deal with mistakes in the mutual instances and how to deal with too many or too few mutual instances. It provides the checking based on the weighted mutual instances considering fault tolerance, gives a way to partition the large-scale mutual instances, and proposes a process greatly reducing the manual annotation work to get more mutual instances. Intension annotation that improves the checking method is also discussed. The method is practical and effective to check subsumption relations between concept queries in different ontologies based on mutual instances.展开更多
Quantitative attributes are partitioned into several fuzzy sets by using fuzzy c-means algorithm.Fuzzy c-means algorithm can embody the actual distribution of the data,and fuzzy sets can soften the partition boundary....Quantitative attributes are partitioned into several fuzzy sets by using fuzzy c-means algorithm.Fuzzy c-means algorithm can embody the actual distribution of the data,and fuzzy sets can soften the partition boundary.Then,we improve the search technology of apriori algorithm and present the algorithm for mining fuzzy association rules.As the database size becomes larger and larger,a better way is to mine fuzzy association rules in parallel.In the parallel mining algorithm,quantitative attributes are partitioned into several fuzzy sets by using parallel fuzzy c-means algorithm.Boolean parallel algorithm is improved to discover frequent fuzzy attribute set,and the fuzzy association rules with at least a minimum confidence are generated on all processors.The experiment results implemented on the distributed linked PC/workstation show that the parallel mining algorithm has fine scaleup,sizeup and speedup.Last,we discuss the application of fuzzy association rules in the classification.The example shows that the accuracy of classification systems of the fuzzy association rules is better than that of the two popular classification methods:C4.5 and CBA.展开更多
基金Supported bythe Outstanding Young Young Scientist’s Fund ofthe National Natural Science Foundation of China (60303024) ,the National Natu-ral Science Foundation of China (90412003) , National Grand Fundamental Re-search 973 Programof China (2002CB312000) , Doctor Foundation of Ministry ofEducation(20020286004) , Opening Foundation of Jiangsu Key Laboratory of Com-puter Information Processing Technology in Soochow University, Natural ScienceResearch Planfor Jiang Su High School(04kjb520096) ,Doctor Foundatoin of Nan-jing University of Posts and Telecommunications(2003-02)
文摘By analyzing the WWW Cache model, we bring forward a user-interest description method based on the fuzzy theory and user-interest inferential relations based on BP（baek propagation） neural network. By this method, the users＇ interest in the WWW cache can be described and the neural network of users＇ interest can be constructed by positive spread of interest and the negative spread of errors. This neural network can infer the users＇ interest. This model is not the simple extension of the simple interest model, but the round improvement of the model and its related algorithm.
基金Supported by the National Natural Science Foundation of China(60373066 , 60425206 , 90412003) , National Grand Fundamental Research 973Programof China(2002CB312000) , National Research Foundationfor the DoctoralProgramof Higher Education of China (20020286004)
文摘Fuzzy ontologics are efficient tools to handle fuzzy and uncertain knowledge on the semantic web; but there are heterogeneity problems when gaining interoperability among different fuzzy ontologies. This paper uses concept approximation between fuzzy ontologies based on instances to solve the heterogeneity problems. It firstly proposes an instance selection technology based on instance clustering and weighting to unify the fuzzy interpretation of different ontologies and reduce the number of instances to increase the efficiency. Then the paper resolves the problem of computing the approximations of concepts into the problem of computing the least upper approximations of atom concepts. It optimizes the search strategies by extending atom concept sets and defining the least upper bounds of concepts to reduce the searching space of the problem. An efficient algorithm for searching the least upper bounds of concept is given.
文摘0 Introduction The surprising growth of the Internet, coupled with the rapid development of Web technique and more and more emergence of web information system and application, is bring great opportunities and big challenges to us. Since the Web provides cross-platform universal access to resources for the massive user population, even greater demand is requested to manage data and services effectively.
基金Supported by the National Natural Sciences Foundation of China(60373066 ,60425206 ,90412003) , National Grand Fundamental Research 973 Pro-gramof China(2002CB312000) , National Research Foundation for the Doctoral Pro-gramof Higher Education of China (20020286004)
文摘This paper proposes a checking method based on mutual instances and discusses three key problems in the method： how to deal with mistakes in the mutual instances and how to deal with too many or too few mutual instances. It provides the checking based on the weighted mutual instances considering fault tolerance, gives a way to partition the large-scale mutual instances, and proposes a process greatly reducing the manual annotation work to get more mutual instances. Intension annotation that improves the checking method is also discussed. The method is practical and effective to check subsumption relations between concept queries in different ontologies based on mutual instances.
基金supported by the National Key Basic Research Program 973(2002CB312000)National Natural Science Funds for Distinguished Young Scholar(60425206)Advanced Armament Research Project(51406020105JB8103).
文摘Quantitative attributes are partitioned into several fuzzy sets by using fuzzy c-means algorithm.Fuzzy c-means algorithm can embody the actual distribution of the data,and fuzzy sets can soften the partition boundary.Then,we improve the search technology of apriori algorithm and present the algorithm for mining fuzzy association rules.As the database size becomes larger and larger,a better way is to mine fuzzy association rules in parallel.In the parallel mining algorithm,quantitative attributes are partitioned into several fuzzy sets by using parallel fuzzy c-means algorithm.Boolean parallel algorithm is improved to discover frequent fuzzy attribute set,and the fuzzy association rules with at least a minimum confidence are generated on all processors.The experiment results implemented on the distributed linked PC/workstation show that the parallel mining algorithm has fine scaleup,sizeup and speedup.Last,we discuss the application of fuzzy association rules in the classification.The example shows that the accuracy of classification systems of the fuzzy association rules is better than that of the two popular classification methods:C4.5 and CBA.
