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Mapping integrity constraint ontology to relational databases

The Journal of China Universities of Posts and Telecommunications 2010年第6期17卷 113-121页

作者： OUYANG Dan-tong College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University Changchun 130012 China

Integrity constraint is a formula that checks whether all necessary information has been explicitly provided. It can be added into ontology to guarantee the data-centric application. In this paper,a set of constraint axioms called IC-mapping axioms are stated. Based on these axioms,a special ontology with integrity constraint,which is adapted to map ontology knowledge to data in relational databases,is defined. It＇s generated through our checking and modification. Making use of the traditional mapping approaches,it can be mapped to relational databases as a normal ontology. Unlike what in the past,a novel mapping approach named IC-based mapping is proposed in accordance with such special ontology. The detailed algorithm is put forward and compared with other existing approaches used in Semantic Web applications. The result shows that our method is advanced to the traditional approaches.

关键词： Semantic Web integrity constraint ontology relational database

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Traffic optimization for distributed network model base on Petri Nets

Traffic optimization for distributed network model base on P...

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Japan-China Joint Workshop on Frontier of Computer Science and Technology (FCST)

作者： Chang, Xin Pang, Huanli Hu, Liang Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University Changchun 130012 China Collage of Computer Science and Engineering Changchun University of Technology Changchun 130012 China

ISBN: (纸本)9780769541396

Petri Nets is a powerful mathematical modeling tool for system description and analysis, with which we can describe the relationship among entities efficiently. The present thesis puts forward the modeling of the distributed computer network DNMPN and DNMPNTO based on Petri Nets. DNMPN implements the basic function of distributed computer network, and DNMPNTO modifies DNMPN's repetitive forward problem. Thus, it is efficiently helpful to optimize the unit traffic of the network, save the net work resource and improve the efficiency of the network system. © 2010 IEEE.

关键词： Petri nets

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A multi-heuristic cooperative ant colony system for optimizing elimination ordering of Bayesian networks

A multi-heuristic cooperative ant colony system for optimizi...

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2010 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2010

作者： Dong, Xuchu Ouyang, Dantong Ye, Yuxin Yu, Haihong Zhang, Yonggang Department of Computer Science and Technology Jilin University Changchun Jilin 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun Jilin 130012 China

ISBN: (纸本)9780769541914

To solve the problem of searching for an optimal elimination ordering of Bayesian networks, a novel effective heuristic, MinSumWeight, and an ACS approach incorporated with multi-heuristic mechanism are proposed. The ACS approach named MHC-ACS utilizes a set of heuristics to direct the ants moving in the search space. The cooperation of multiple heuristics helps ants explore more regions. Moreover, the most appropriate heuristic will be identified and be reinforced with the evolution of the whole system. Experiments demonstrate that MHC-ACS has a better performance than other swarm intelligence methods. © 2010 IEEE.

关键词： Bayesian networks

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Block-based normalized-cut algorithm for image segmentation

Block-based normalized-cut algorithm for image segmentation

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作者： Song, Haiyu Li, Xiongfei Wang, Pengjie Chen, Jingrun Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Institute of Computer Graphics and Image Processing Dalian Nationalities University Dalian China State Key Laboratory of CAD and CG Zhejiang University Hangzhou China

ISBN: (纸本)9783642051722

Automatic image segmentation remains a challenging problem in the fields of computer vision, image analysis and understanding. A lot of algorithms and technologies have been proposed and developed for image segmentation, among which the N-cut has been a promising method and active research area due to better quality. But the N-cut can't process medium and large image dataset online due to incredible memory and time complexity. Moreover, the N-cut algorithm can't utilize texture information since its basic unit of clustering algorithm is pixel. To overcome the drawback and improve the description power, the paper proposes a block-based N-cut algorithm for image segmentation, whose basic unit of clustering is image block instead of individual pixel. Block-based algorithm not only decreases the time and storage complexity, but also improves the discrimination power of visual feature vector. The experiments demonstrate that proposed algorithm outperform the original N-cut algorithm in efficiency. Experiments show that the proposed block-based N-cut algorithm is more suitable for online processing large-scale data. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Image segmentation

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An efficient and effective automatic image annotation using positive and negative example images

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ICIC Express Letters 2011年第8 B期5卷 2927-2932页

作者： Song, Haiyu Li, Xiongfei Wang, Pengjie Bao, Cuizhu Dong, Yuying Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University No. 2699 Qianjin Avenue Changchun 130012 China Institute of Computer Graphics and Image Processing Dalian Nationalities University No. 18 Liaohe Road Eco. and Tech. Dev. Zone Dalian 116600 China College of Computer Science and Engineering Dalian Nationalities University No. 18 Liaohe Road Eco. and Tech. Dev. Zone Dalian 116600 China College of Life Science Dalian Nationalities University No. 18 Liaohe Road Eco. and Tech. Dev. Zone Dalian 116600 China

Automatic image annotation is an active topic and difficult task in computer vision domain, which has attracted more and more researchers' attention. Many approaches have been proposed to automatically annotate images. To improve the annotation quality, most of them have profound principlessophisticated modelsand high time complexities. How to efficiently annotate image is becoming one key issue in the field of computer vision. We propose a very efficient and effective approach called keyword vector classification approach, whose model is very simple. Experiments on Corel5K dataset show that the annotation performance of the our proposed approach is almost four times better than translation model and twice better than CMRM (in terms of precision, recall and F1) while the time cost of ours surprisingly decreases. In addition, to our knowledge, our proposed approach outperforms most of existing ones in terms of N+. © 2011 ISSN 1881-803X.

关键词： Image retrieval

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Mining data records based on ontology evolution for deep web

Mining data records based on ontology evolution for deep web

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2010 2nd International Conference on Computer engineering and Technology, ICCET 2010

作者： Wen, Zhang Chen, Kerui Fan, Zhang College of Information Engineering Zhongzhou University Zhengzhou 450044 China College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9781424463503

Currently, the research for the extraction of information in deep web is pretty active. Although many researchers already adopted ontology in the data extraction, many problems still exist. This paper proposed an ontology evolution based method for mining in the data area. Not only will this method solve the problem when the website only consists of one record, but it also can identify he meaning of data that has no labels. With the evolution of ontology, the extraction of data records is being more accurate. Experiments indicate that this method could improve the accuracy and efficiency of data extraction. © 2010 IEEE.

关键词： Ontology

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A diversity-guided heuristic-based genetic algorithm for triangulation of Bayesian networks

A diversity-guided heuristic-based genetic algorithm for tri...

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作者： Dong, Xuchu Yu, Haihong Ouyang, Dantong Ye, Yuxin Zhang, Yonggang Department of Computer Science and Technology Jilin University Changchun Jilin 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun Jilin 130012 China

ISBN: (纸本)9788988678251

For the optimization problem about triangulation of Bayesian networks, a novel genetic algorithm, DHGA, is proposed in this paper. DHGA employs a heuristic-based mutation operation. Moreover, it uses population diversity to identify stagnation and convergence as well as to guide the search procedure. Experiments on representative benchmarks show that DHGA posses better performance and robustness than other swarm intelligence methods.

关键词： Bayesian networks

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Genetic algorithm with affinity propagation

Genetic algorithm with affinity propagation

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2010 IEEE International Conference on Information and Automation, ICIA 2010

作者： Wu, Chunguo Gao, Hao Yu, Lianjiang Liang, Yanchun Xiang, Rongwu College of Computer Science and Technology Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry Jilin University Changchun 130012 China School of Basic Courses Shenyang Pharmaceutical University Shenyang 110016 China

ISBN: (纸本)9781424457021

Classical genetic algorithm suffers heavy pressure of fitness evaluation for time-consuming optimization problems, e.g., aerodynamic design optimization, qualitative model learning in bioinformatics. To address this problem, we present a combination between genetic algorithms and clustering methods. Specifically, the clustering method used in this paper is affinity propagation. The numerical experiments demonstrate that the proposed method performs promisingly for well-known benchmark problems in the term of optimization accuracy. ©2010 IEEE.

关键词： Genetic algorithms

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An ontology-based schema matching on deep web

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Journal of computational Information Systems 2010年第4期6卷 1077-1084页

作者： Zhang, Aiqi Zuo, Wanli Wang, Ying Ji, Wenyan Peng, Tao Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China

A large number of techniques have been implemented mostly based on abundance of data statistics and they usually do not harness the semantic relationships between attributes. This paper proposes a novel ontology-based schema matching approach. First, a core domain ontology has been built through analyzing pages from numerous domains. Then a two-step schema-matching process is presented to identify the correct matches between schema attributes and ontology information from the semantic perspective and this process can handle not only simple matches but also complex matches. Within the matching process, the ontology is real-time updated to make the subsequent matching processes easier. Finally, 100 interfaces about four different domains in the UIUC dataset are used for evaluating our approach and the experimental results show that it can improve the accuracy and the efficiency of schema matching among multiple interfaces. Copyright © 2010 Binary Information Press.

关键词： Ontology

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A hybrid algorithm of minimum spanning tree and nearest neighbor for classifying human cancers

A hybrid algorithm of minimum spanning tree and nearest neig...

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2010 3rd International Conference on Advanced Computer Theory and engineering, ICACTE 2010

作者： Zhou, Chunbao Wan, Liming Liang, Yanchun College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 China Research Institute on General Development and Evaluation of Equipment EAAF of PLA Beijing 100076 China

ISBN: (纸本)9781424465408

Classification and prediction of different cancers based on gene expression profiles are important for cancer diagnosis, cancer treatment and medication discovery. The k nearest neighbor algorithm (k-NN) is one easy and efficient machine learning method for cancer classification and the parameter k is crucial. In this paper, we integrate minimum spanning tree (MST) and k nearest neighbor algorithm (k-NN) for cancer classification. The MST is designed for the selection of parameter k and the nearest neighbors for k-NN. Firstly we build a minimum spanning tree (MST) based on Euclidean distance between each two samples for gene expression data only including one unknown class sample. Secondly for unknown class sample in the gene expression data, we find the connected samples and then apply majority vote principle. Thirdly if there are tied votes then we expend the connected samples with the nearest neighbors for unknown class sample until all the samples are expended or the class for unknown sample is obtained. This hybrid algorithm is referred to as MSTNN. The hybrid algorithm MSTNN is compared with kNN and other 3 existing classification algorithms on CNS dataset, Colon dataset and Lung dataset, 3 binary class gene expression datasets and 3 multi-class gene expression datasets: Leukemial, Leukemia2, and Leukemia3 *** human cancers. The MSTNN algorithm improves 5.65% better than k-NN on average LOOCV accuracy and 13.80% better than kNN on testing datasets classification average accuracy, and achieves the best performance in all the 5 algorithms. The results demonstrate that the proposed MSTNN algorithm is feasible to classify human cancers. © 2010 IEEE.

关键词： Classification (of information)

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