A formal representation of ontologies is proposed, based on F-logic and O-logic; and the works in the building of ontologies in NKI. An ontology includes class frames, slot frames, class-slot frames, object frames and...
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A formal representation of ontologies is proposed, based on F-logic and O-logic; and the works in the building of ontologies in NKI. An ontology includes class frames, slot frames, class-slot frames, object frames and axioms. The value restrictions of slots are defined in slot frames. For each slot and each class, there is a class-slot frame representing the specific value restrictions of the slot when defining the class; and the relations between class-slot frames and slot frames are discussed. For a slot in a class frame, its values are inherited to its subclasses without blocking; and its default values are inherited to its subclasses taking overriding, revising and conflict resolution into account. After giving the formal representation of ontologies, the semantics of ontologies are discussed, and main results are presented.
A knowledge flow is invisible but it plays an important role in ordering knowledge exchange in teamwork. It can help achieve effective team knowledge management by modeling, optimizing, monitoring and controlling the ...
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A knowledge flow is invisible but it plays an important role in ordering knowledge exchange in teamwork. It can help achieve effective team knowledge management by modeling, optimizing, monitoring and controlling the operation of knowledge flow processes. This paper proposes the notion of knowledge energy as the driving cause of forming an autonomous knowledge flow network, and explores the behind principles. Knowing these principles can help team managers and the support systems improve cooperation by monitoring the knowledge energy of nodes, and by evaluating and adjusting knowledge flows. A knowledge flow network management system can be the high layer of the knowledge grid to help improve the efficiency of distributed knowledge-intensive teamwork.
Feature extraction or selection is one of the most important steps in pattern recognition or pattern classification, data mining, machine learning and so on. In this paper, we introduce the information theory, propose...
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Feature extraction or selection is one of the most important steps in pattern recognition or pattern classification, data mining, machine learning and so on. In this paper, we introduce the information theory, propose a new concept of probability information distance (PID) and prove that the PID satisfies four requests of axiomatization of the distance. So the PID is a kind of distance measure, which can be used to measure the degree of variation between two random variables. We make the PID be separability criterion of the classes for information feature extraction, and call it PID criterion (PIDC). Based on PIDC, we design a novel algorithm for information feature extraction. Compared with principal components analysis (PCA), correlation analysis etc., the algorithm put forward in this paper had regarded for the class information, and so it is a kind of supervised algorithm of feature extraction. The experimental results demonstrate that the algorithm is valid and reliable, and it provides a new research approach for feature extraction, data mining and pattern recognition.
Principal component analysis (PCA) is an important method in multivariate statistical analysis, and its main idea is compression of dimensionality including variables and samples. In this paper, based on the ideas con...
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Principal component analysis (PCA) is an important method in multivariate statistical analysis, and its main idea is compression of dimensionality including variables and samples. In this paper, based on the ideas concerned with information function and information entropy of Shannon information theory, consider the inherent characteristic of eigenvalues of matrix, two new concepts of possibility information function (PIF) and possibility information entropy (PIE) are proposed firstly. On the basis of these, the formulae of information rate (IR) and accumulated information rate (AIR) are set up, by which the degree of information compression is measured. In the end, we improve the PCA algorithm called improved principal component analysis (PCA). Through simulated application in practice, the results show that the IPCA proposed here is efficient and satisfactory. It provides a new research approach of information feature compression for pattern recognition.
In this paper, we present a brief summary to 3D mesh model segmentation techniques, including definition, latest achievements, classification and application in this field. Then evaluations on some of typical methods,...
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In this paper, we present a brief summary to 3D mesh model segmentation techniques, including definition, latest achievements, classification and application in this field. Then evaluations on some of typical methods, such as Watershed, topological and geometrical method, are introduced. After some applications are presented, problems and prospect of the techniques are also discussed.
This paper presents a novel segmentation method based on a non-parametric background model that has the ability of modeling multi-model. Firstly, both the intensity and edge features are used to improve robustness of ...
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This paper presents a novel segmentation method based on a non-parametric background model that has the ability of modeling multi-model. Firstly, both the intensity and edge features are used to improve robustness of the foreground detection. Secondly, we also present an adaptive shadow detection model to find the accurate moving objects. The experiment results show that our proposed method is effective.
Based on the quotient space granular theorem, the image segmentation concept is analyzed and the image segmentation methods are studied, and then the quotient space granular theorem of image segmentation is demonstrat...
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Based on the quotient space granular theorem, the image segmentation concept is analyzed and the image segmentation methods are studied, and then the quotient space granular theorem of image segmentation is demonstrated. The image segmentation problems are described with triple elements function of the quotient space model (X,f,Γ)⇔([X],[f],[Γ]), according to the quotient space granularity computing, the image segmentation theorem is presented. The methods of images segmentation based on hierarchical and synthesis and combinational technique are exploited and then the segmentation algorithm based granularity synthesis technique is proposed. In this technique, the features of directionality and roughness in texture images data set are firstly extracted respectively to form the different granularities of image, then the different granularity are synthesized according to the theorem of granularity synthesis, finally the texture images is segmented. The experimental results demonstrate that the algorithm is valid for the segmentation of complicated texture images.
The future Web cart be imagined as a life network consisting of resource nodes and semantic relationship links between them. Any node has a life span from birth - adding it to the network - to death - removing it from...
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ISBN:
(纸本)158113844X
The future Web cart be imagined as a life network consisting of resource nodes and semantic relationship links between them. Any node has a life span from birth - adding it to the network - to death - removing it from the network. Through establishing and investigating two types of models for such a network, we obtain the same scale free distribution of semantic links. Simulations and comparisons validate the rationality of the proposed models.
An important obstacle to the success of the Semantic Web is that the establishment of the semantic relationship is labor-intensive. This paper proposes an automatic semantic relationship discovering approach for const...
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ISBN:
(纸本)158113844X
An important obstacle to the success of the Semantic Web is that the establishment of the semantic relationship is labor-intensive. This paper proposes an automatic semantic relationship discovering approach for constructing the semantic link network. The basic premise of this work is that the semantics of a web page can be reflected by a set of keywords, and the semantic relationship between two web pages can be determined by the semantic relationship between their keyword sets. The approach adopts the data mining algorithms to discover the semantic relationships between keyword sets, and then uses deductive and analogical reasoning to enrich the semantic relationships. The proposed algorithms have been implemented. Experiment shows that the approach is feasible.
Based on rank-1 update, Sparse Bayesian Learning Algorithm (SBLA) is proposed. SBLA has the advantages of low complexity and high sparseness, being very suitable for large scale problems. Experiments on synthetic and ...
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