In the course of running an artificial intelligent system many redundant rules are often produced. To refine the knowledge base, viz. to remove the redundant rules, can accelerate the reasoning and shrink the rule bas...In the course of running an artificial intelligent system many redundant rules are often produced. To refine the knowledge base, viz. to remove the redundant rules, can accelerate the reasoning and shrink the rule base. The purpose of the paper is to present the thinking on the topic and design the algorithm to remove the redundant rules from the rule base. The “abstraction” of “state variable”, redundant rules and the least rule base are discussed in the paper. The algorithm on refining knowledge base is also presented.展开更多
Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results conta...Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining(GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.展开更多
In this paper, the authors propose a method that incorporates mechanisms for handling ambiguity in speech and the ability of humans to create associations, and for formulating conversations based on rule base knowledg...In this paper, the authors propose a method that incorporates mechanisms for handling ambiguity in speech and the ability of humans to create associations, and for formulating conversations based on rule base knowledge and common knowledge. Go beyond the level that can be achieved, using only conventional natural language processing and vast repositories of sample patterns. In this paper, the authors propose a method for computer conversation sentences generated using newspaper headlines as an example of how the common knowledge and associative ability are applied.展开更多
Currently, knowledge-based sharing and service system has been a hot issue and knowledge fusion, especially for implicit knowledge discovery, becomes the core of knowledge processing and optimization in the system. In...Currently, knowledge-based sharing and service system has been a hot issue and knowledge fusion, especially for implicit knowledge discovery, becomes the core of knowledge processing and optimization in the system. In the research, a knowledge fusion framework based on agricultural ontology and fusion rules was pro- posed, including knowledge extraction, clearing and annotation modules based on a- gricultural ontology, fusion rule construction, choosing and evaluation modules based on agricultural ontology and knowledge fusion module for users＇ demands. Finally, the significance of the framework to system of agricultural knowledge services was proved with the help of a case.展开更多
The minimal unsatisfiability-preserving sub-TBoxes（MUPS）of an unsatisfiable class C identified by two equivalent transformations,axiom splitting and ontology reduction,and three discrimination rules comprise minimal...The minimal unsatisfiability-preserving sub-TBoxes（MUPS）of an unsatisfiable class C identified by two equivalent transformations,axiom splitting and ontology reduction,and three discrimination rules comprise minimal sets of axioms which support the unsatisfiability.Discrimination rules classify all MUPS into three types based on the transitivity of unsatisfiability,fully dependent on C（MUPSf）,transitively dependent on C（MUPSt）and uncertainly dependent on C（MUPSu）.The results show that the number of MUPSt is frequently a large fraction of the total number of all MUPS,but only MUPSf catches the root error of C.Modelers and domain experts conduct iterative repair work effectively,considering only MUPSf in each iteration.The classification shows the significance for the evaluation of the quality of ontologies from the perspective of maintenance and for repair work.展开更多
This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and Bayesian predictive modeling, respectively to generate knowledge from tra...This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and Bayesian predictive modeling, respectively to generate knowledge from training data. With these methods, building a knowledge base for automated soil mapping is easier than using the conventional knowledge acquisition approach. The knowledge bases built by these two methods were used by the knowledge classifier for soil type classification of the Longyou area, Zhejiang Province, China using TM bi-temporal imageries and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on field survey. The accuracy assessment and analysis of the resultant soil maps suggested that the knowledge bases built by these two methods were of good quality for mapping distribution model of soil classes over the study area.展开更多
This paper proposes the principle of comprehensive knowledge discovery. Unlike most of the current knowledge discovery methods, the comprehensive knowledge discovery considers both the spatial relations and attributes...This paper proposes the principle of comprehensive knowledge discovery. Unlike most of the current knowledge discovery methods, the comprehensive knowledge discovery considers both the spatial relations and attributes of spatial entities or objects. We introduce the theory of spatial knowledge expression system and some concepts including comprehensive knowledge discovery and spatial union information table (SUIT). In theory, SUIT records all information contained in the studied objects, but in reality, because of the complexity and varieties of spatial relations, only those factors of interest to us are selected. In order to find out the comprehensive knowledge from spatial databases, an efficient comprehensive knowledge discovery algorithm called recycled algorithm (RAR) is suggested.展开更多
The paper focuses on amalgamation of automata theory and fuzzy language. It uses adaptive knowledge based abstract framework which uses dynamic neural network framework along with fuzzy automata as Models of Learning,...The paper focuses on amalgamation of automata theory and fuzzy language. It uses adaptive knowledge based abstract framework which uses dynamic neural network framework along with fuzzy automata as Models of Learning, combining the two methodologies the authors develop a new framework termed as Fuzzy Automata based Neural Network （FANN）. It highlights conversion of knowledge rule to fuzzy automata thereby generating a framework FANN. FANN consists of composite fuzzy automation divided into ＂Performance Evaluator＂ and ＂Feature Extraction＂ which takes the help of previously stored samples of similar situations. The authors have extended FANN for Urban Traffic Modeling.展开更多
This paper introduces a method of building a prototype system of geologic profile auto-drawing.A.NET development platform and integrated environment was used along with a component based design,a B/S system model,and ...This paper introduces a method of building a prototype system of geologic profile auto-drawing.A.NET development platform and integrated environment was used along with a component based design,a B/S system model,and XML techniques.Knowledge rules for creating geologic profiles and generating virtual drilling data from existing bore data and expert,hand-drawn geologic profiles were acquired. Then a prototype system was established by utilizing the known knowledge rules,topological relationships, and semantic relationships among strata.This system has a friendly human-computer interface and can meet requirements of mutual queries between attribute and spatial data.The generated profile map is editable.This study provides a new powerful tool for underground mine work.展开更多
The teachers use explicit learning in the traditional teaching of English grammar, which pays great attention to the master of grammar knowledge points. Nowadays, some English teachers excessively emphasize the import...The teachers use explicit learning in the traditional teaching of English grammar, which pays great attention to the master of grammar knowledge points. Nowadays, some English teachers excessively emphasize the importance of teaching grammar imperceptibly, but neglect to explain the rules of grammar. The diametrically isolation of implicit and explicit learning doesn＇t correspond with the English grammar teaching. So the teachers should combine the two learning styles in order to make the best use of them to teach English grammar.展开更多
The paper considers the problem of semantic processing of web documents by designing an approach, which combines extracted semantic document model and domain- related knowledge base. The knowledge base is populated wi...The paper considers the problem of semantic processing of web documents by designing an approach, which combines extracted semantic document model and domain- related knowledge base. The knowledge base is populated with learnt classification rules categorizing documents into topics. Classification provides for the reduction of the dimensio0ality of the document feature space. The semantic model of retrieved web documents is semantically labeled by querying domain ontology and processed with content-based classification method. The model obtained is mapped to the existing knowledge base by implementing inference algorithm. It enables models of the same semantic type to be recognized and integrated into the knowledge base. The approach provides for the domain knowledge integration and assists the extraction and modeling web documents semantics. Implementation results of the proposed approach are presented.展开更多
Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities...Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities in intelligent data analyzing applications are mostly represented with the help of IF-THEN rules. With the help of these rules the following tasks are solved： prediction, classification, pattern recognition and others. Using different approaches---clustering algorithms, neural network methods, fuzzy rule processing methods--we can extract rules that in an understandable language characterize the data. This allows interpreting the data, finding relationships in the data and extracting new rules that characterize them. Knowledge acquisition in this paper is defined as the process of extracting knowledge from numerical data in the form of rules. Extraction of rules in this context is based on clustering methods K-means and fuzzy C-means. With the assistance of K-means, clustering algorithm rules are derived from trained neural networks. Fuzzy C-means is used in fuzzy rule based design method. Rule extraction methodology is demonstrated in the Fisher＇s Iris flower data set samples. The effectiveness of the extracted rules is evaluated. Clustering and rule extraction methodology can be widely used in evaluating and analyzing various economic and financial processes.展开更多
With the gradual deepening of study on the parallel mechanism,the difficulty brought by the existence of coupling to the theoretical analysis and practical application of parallel mechanisms is becoming increasingly a...With the gradual deepening of study on the parallel mechanism,the difficulty brought by the existence of coupling to the theoretical analysis and practical application of parallel mechanisms is becoming increasingly apparent.The research on the decoupled parallel mechanism is currently one of the hot fields.Though most of the rotational parallel mechanisms,which has been widely used in spatial orientation fields,are not decoupled.It is comparative difficult for the synthesis of fully decoupled rotational parallel mechanisms,and the number of the existing parallel mechanisms which can realize rotational decoupling is limited.In addition,most of the existing rotational decoupled parallel mechanism are obtained depending on the experience of the researcher,and don't possess the general theoretical significance.Based on the screw theory,this paper presents the rotational conditions of the parallel mechanism through the analysis of the relationship between the degree of freedom of the parallel mechanism and its limbs.The synthesis rule of the limbs for decoupled rotational parallel mechanism is established according to the twist screw system of the limbs,which assures the decoupling of the rotations in each limb.The selection principle of the input pairs for the rotation driven limbs is proposed,then the type synthesis method for rotational decoupled parallel mechanisms is formed.With this type synthesis method,synthesis of the rotational decoupled parallel mechanisms is performed,which can provide a reference for the development of the novel type parallel mechanisms with independent intellectual property rights.展开更多
基金Supported by the National Education Science Foundation of China under Grant(No.104086)
文摘In the course of running an artificial intelligent system many redundant rules are often produced. To refine the knowledge base, viz. to remove the redundant rules, can accelerate the reasoning and shrink the rule base. The purpose of the paper is to present the thinking on the topic and design the algorithm to remove the redundant rules from the rule base. The “abstraction” of “state variable”, redundant rules and the least rule base are discussed in the paper. The algorithm on refining knowledge base is also presented.
基金Under the auspices of Special Fund of Ministry of Land and Resources of China in Public Interest(No.201511001)
文摘Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining(GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.
文摘In this paper, the authors propose a method that incorporates mechanisms for handling ambiguity in speech and the ability of humans to create associations, and for formulating conversations based on rule base knowledge and common knowledge. Go beyond the level that can be achieved, using only conventional natural language processing and vast repositories of sample patterns. In this paper, the authors propose a method for computer conversation sentences generated using newspaper headlines as an example of how the common knowledge and associative ability are applied.
基金Supported by Specialized Funds of CASIndividual Service System of Agricultural Information in Tibet(2012-J-08)+1 种基金Science and Technology Funds of CASMultimedia Information Service in Rural Area based on 3G Information Terminal(201219)~~
文摘Currently, knowledge-based sharing and service system has been a hot issue and knowledge fusion, especially for implicit knowledge discovery, becomes the core of knowledge processing and optimization in the system. In the research, a knowledge fusion framework based on agricultural ontology and fusion rules was pro- posed, including knowledge extraction, clearing and annotation modules based on a- gricultural ontology, fusion rule construction, choosing and evaluation modules based on agricultural ontology and knowledge fusion module for users＇ demands. Finally, the significance of the framework to system of agricultural knowledge services was proved with the help of a case.
基金The Science and Technology Development Program of Tianjin(No.06YFGZGX05900)
文摘The minimal unsatisfiability-preserving sub-TBoxes（MUPS）of an unsatisfiable class C identified by two equivalent transformations,axiom splitting and ontology reduction,and three discrimination rules comprise minimal sets of axioms which support the unsatisfiability.Discrimination rules classify all MUPS into three types based on the transitivity of unsatisfiability,fully dependent on C（MUPSf）,transitively dependent on C（MUPSt）and uncertainly dependent on C（MUPSu）.The results show that the number of MUPSt is frequently a large fraction of the total number of all MUPS,but only MUPSf catches the root error of C.Modelers and domain experts conduct iterative repair work effectively,considering only MUPSf in each iteration.The classification shows the significance for the evaluation of the quality of ontologies from the perspective of maintenance and for repair work.
基金Project supported by the National Natural Science Foundation ofChina (No. 40101014) and by the Science and technology Committee of Zhejiang Province (No. 001110445) China
文摘This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and Bayesian predictive modeling, respectively to generate knowledge from training data. With these methods, building a knowledge base for automated soil mapping is easier than using the conventional knowledge acquisition approach. The knowledge bases built by these two methods were used by the knowledge classifier for soil type classification of the Longyou area, Zhejiang Province, China using TM bi-temporal imageries and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on field survey. The accuracy assessment and analysis of the resultant soil maps suggested that the knowledge bases built by these two methods were of good quality for mapping distribution model of soil classes over the study area.
基金theChina’sNationalSurveyingTechnicalFund (No .2 0 0 0 7)
文摘This paper proposes the principle of comprehensive knowledge discovery. Unlike most of the current knowledge discovery methods, the comprehensive knowledge discovery considers both the spatial relations and attributes of spatial entities or objects. We introduce the theory of spatial knowledge expression system and some concepts including comprehensive knowledge discovery and spatial union information table (SUIT). In theory, SUIT records all information contained in the studied objects, but in reality, because of the complexity and varieties of spatial relations, only those factors of interest to us are selected. In order to find out the comprehensive knowledge from spatial databases, an efficient comprehensive knowledge discovery algorithm called recycled algorithm (RAR) is suggested.
文摘The paper focuses on amalgamation of automata theory and fuzzy language. It uses adaptive knowledge based abstract framework which uses dynamic neural network framework along with fuzzy automata as Models of Learning, combining the two methodologies the authors develop a new framework termed as Fuzzy Automata based Neural Network （FANN）. It highlights conversion of knowledge rule to fuzzy automata thereby generating a framework FANN. FANN consists of composite fuzzy automation divided into ＂Performance Evaluator＂ and ＂Feature Extraction＂ which takes the help of previously stored samples of similar situations. The authors have extended FANN for Urban Traffic Modeling.
基金supported by the National Department Public Benefit Research Foundation(No. GYHY200806002)the Nanjing University of Information Science & Technology Research Fund(No.S8108185001)Project supported by NSFC(No.40901244).
文摘This paper introduces a method of building a prototype system of geologic profile auto-drawing.A.NET development platform and integrated environment was used along with a component based design,a B/S system model,and XML techniques.Knowledge rules for creating geologic profiles and generating virtual drilling data from existing bore data and expert,hand-drawn geologic profiles were acquired. Then a prototype system was established by utilizing the known knowledge rules,topological relationships, and semantic relationships among strata.This system has a friendly human-computer interface and can meet requirements of mutual queries between attribute and spatial data.The generated profile map is editable.This study provides a new powerful tool for underground mine work.
文摘The teachers use explicit learning in the traditional teaching of English grammar, which pays great attention to the master of grammar knowledge points. Nowadays, some English teachers excessively emphasize the importance of teaching grammar imperceptibly, but neglect to explain the rules of grammar. The diametrically isolation of implicit and explicit learning doesn＇t correspond with the English grammar teaching. So the teachers should combine the two learning styles in order to make the best use of them to teach English grammar.
文摘The paper considers the problem of semantic processing of web documents by designing an approach, which combines extracted semantic document model and domain- related knowledge base. The knowledge base is populated with learnt classification rules categorizing documents into topics. Classification provides for the reduction of the dimensio0ality of the document feature space. The semantic model of retrieved web documents is semantically labeled by querying domain ontology and processed with content-based classification method. The model obtained is mapped to the existing knowledge base by implementing inference algorithm. It enables models of the same semantic type to be recognized and integrated into the knowledge base. The approach provides for the domain knowledge integration and assists the extraction and modeling web documents semantics. Implementation results of the proposed approach are presented.
文摘Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities in intelligent data analyzing applications are mostly represented with the help of IF-THEN rules. With the help of these rules the following tasks are solved： prediction, classification, pattern recognition and others. Using different approaches---clustering algorithms, neural network methods, fuzzy rule processing methods--we can extract rules that in an understandable language characterize the data. This allows interpreting the data, finding relationships in the data and extracting new rules that characterize them. Knowledge acquisition in this paper is defined as the process of extracting knowledge from numerical data in the form of rules. Extraction of rules in this context is based on clustering methods K-means and fuzzy C-means. With the assistance of K-means, clustering algorithm rules are derived from trained neural networks. Fuzzy C-means is used in fuzzy rule based design method. Rule extraction methodology is demonstrated in the Fisher＇s Iris flower data set samples. The effectiveness of the extracted rules is evaluated. Clustering and rule extraction methodology can be widely used in evaluating and analyzing various economic and financial processes.
基金supported by the National Natural Science Foundation of China (Grant Nos. 50875227, 51005195)
文摘With the gradual deepening of study on the parallel mechanism,the difficulty brought by the existence of coupling to the theoretical analysis and practical application of parallel mechanisms is becoming increasingly apparent.The research on the decoupled parallel mechanism is currently one of the hot fields.Though most of the rotational parallel mechanisms,which has been widely used in spatial orientation fields,are not decoupled.It is comparative difficult for the synthesis of fully decoupled rotational parallel mechanisms,and the number of the existing parallel mechanisms which can realize rotational decoupling is limited.In addition,most of the existing rotational decoupled parallel mechanism are obtained depending on the experience of the researcher,and don't possess the general theoretical significance.Based on the screw theory,this paper presents the rotational conditions of the parallel mechanism through the analysis of the relationship between the degree of freedom of the parallel mechanism and its limbs.The synthesis rule of the limbs for decoupled rotational parallel mechanism is established according to the twist screw system of the limbs,which assures the decoupling of the rotations in each limb.The selection principle of the input pairs for the rotation driven limbs is proposed,then the type synthesis method for rotational decoupled parallel mechanisms is formed.With this type synthesis method,synthesis of the rotational decoupled parallel mechanisms is performed,which can provide a reference for the development of the novel type parallel mechanisms with independent intellectual property rights.
