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Journal of Computational Information Systems 2008年第5期4卷 2237-2244页

作者： Li, Weijiang Zhao, Tiejun Zang, Wenmao Computer Application Key Laboratory of Yunnan Province Kunming University of Science and Technology Kunming 650051 China School of Computer Science Haerbin Institute of Technology Harbin 150001 China

This paper presents an investigation into the utility of document summarization in the context of information retrieval. The investigation explores the use of both context-independent standard summaries and query-biased summaries. We developed a generic, a query-based, and a hybrid summarizer, each with differing amounts of document context. The generic summarizer used a blend of discourse information and information obtained through traditional surface-level analysis. We evaluate these summaries through summary-based retrieval models proposed by author on several TREC collections. We show that it is worse that retrieval documents with summaries directly. However, summary-based retrieval model, smoothing document model with summary model, can perform consistently across collections of realistic size, and significant improvements over document-based retrieval can be obtained in a fully automatic manner and without relevance information provided by human. © 2008 Binary Information Press.

关键词： Information retrieval

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Apical-dominant particle swarm optimization

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Progress in Natural Science:Materials International 2008年第12期18卷 1577-1582页

作者： Zhihua Cui~(a,b) Xingjuan Cai~b Jianchao Zeng~b Guoji Sun~a a State key laboratory for Manufacturing Systems Engineering,Xi’an Jiaotong University,Xi’an 710049,China b Division of System Simulation and computer application,Taiyuan University of Science and technology,Taiyuan 030024,China State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Xi’an 710049 China Division of System Simulation and Computer Application Taiyuan University of Science and Technology Taiyuan 030024 China

Particle swarm optimization (PSO) is a new stochastic population-based search methodology by simulating the animal social behaviors such as birds flocking and fish *** improvements have been proposed within the framework of this biological assumption. However,in this paper,the search pattern of PSO is used to model the branch growth process of natural *** provides a different poten- tial manner from artificial *** illustrate the effectiveness of this new model,apical dominance phenomenon is introduced to construct a ncvel variant by emphasizing the influence of the *** this improvement,the population is divided into three different kinds of buds associated with their ***,a mutation strategy is applied to enhance the ability escaping from a local ***- ulation results demonstrate good performance of the new method when solving high-dimensional multi-modal problems.

关键词： Apical-dominance phenomenon Particle swarm optimization Branch growth model High-dimensional multi-modal benchmarks

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Multivariate classification of deep web sources based on maximum entropy model

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Journal of Computational Information Systems 2008年第3期4卷 1093-1100页

作者： Huang, Li Zhao, Pengpeng Cui, Zhiming Wei, Fang Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China Jiangsu Key Laboratory of Computer Information Processing Technology Soochow University Suzhou 215006 China

With the fast development of World Wide Web, the quantity of web information is increasing in an unprecedented pace, a great many of which are generated dynamically from background databases, and can't be indexed by traditional search engine, so we call them Deep Web. For the heterogeneous and dynamic features of Deep Web sources, classifying the Deep Web source by domain effectively is a significant precondition of Deep Web sources integration. In this paper, we consider the visible features of Deep Web and Maximum Entropy approach, and then on the basis of binary classification, we propose a new multivariate classification approach based on Maximum Entropy towards Deep Web sources. In addition, we propose a Feedback algorithm to improve the accuracy of classification. An experimental evaluation over real Web data shows that, our approach could provide an effective and general solution to the multivariate classification of Deep Web sources.

关键词： Search engines

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Restricted-domain question classification with the help of syntactic structures relationships and domain characteristic

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Journal of Computational Information Systems 2008年第6期4卷 2659-2665页

作者： Meng, Xiangyan Yu, Zhengtao Zhang, Cheng Li, Yingwei Guo, Jianyi Mao, Cunli School of Information Engineering and Automation Kunming University of Science and Technology Kunming 650051 China Computer Technology Application Key Laboratory of Yunnan Province B The Institute of Intelligent Information Processing Kunming 650051 China

Question classification is an important step in a question answering system, the accuracy rate of question classification has great impact to a question answering system's following module. This paper proposed a question classification method which union syntactic structures relationships and domain characteristic. This method chooses sentence trunk and domain terms as classified characteristic, uses improved Bayes classify learning algorithm to structure question classification model. This paper has realized question classification in Yunnan tourism question answering system. Experiment results showed that the method is effective;the accuracy rate is 87.4% in 5 coarse and 79.5% in 23 fine in Yunnan traveling domain. © 2008 Binary Information Press.

关键词： Syntactics

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Forecasting system based on Wavelet Transform and PSO-SVM

Forecasting system based on Wavelet Transform and PSO-SVM

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IEEE International Workshop on Anti-counterfeiting, Security, Identification, ASID

作者： Qisong Chen Yun Wu Xin Zhang Xiaowei Chen School of Computer Science and Technology Guizhou University Guiyang Guizhou China GuiZhou Key Laboratory for Photoelectric Technology and Application Guiyang Guizhou China

A new method for load forecasting based on LS-SVM, PSO and wavelet transform is proposed. The wavelet transform is adopted to decompose the historical data, so the approximate part and several detail parts are obtained. The results of wavelet transform are predicted by a separate LS-SVM predictor. PSO is employed to determine these parameters of SVM model. The novel forecast model integrates the advantage of WT, PSO and LS-SVM. Compared with other predictors, this forecast model has greater generality ability and higher accuracy.

关键词： Wavelet transforms Load forecasting Discrete wavelet transforms Predictive models Autoregressive processes computer science Support vector machines Least squares methods Particle swarm optimization Stochastic processes

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The self-adaption strategy for parameter ε in ε-MOEA

The self-adaption strategy for parameter ε in ε-MOEA

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Congress on Evolutionary Computation

作者： Min Zhang Wenjian Luo Xingxin Pei Xufa Wang Nature Inspired Computing and Application Laboratory Department of Computer Science and Technology University of Science and Technology China Anhui Key Laboratory of software in Computing Communication University of Science and Technology China University of Science and Technology of China Hefei Anhui CN Nature Inspired Computing and Application Laboratory Department of Computer Science and Technology of China University of Science and Technology China

A novel self-adaption strategy for the parameter epsiv in epsiv-MOEA is proposed in this paper based on the analyses of the relationship between the value of epsiv and the maximum number of non-dominated solutions. Then this novel strategy is applied in epsiv-MOEA and tested on 10 common benchmark functions. The experimental results demonstrate that even if without the good initial value for the parameter s, epsiv-MOEA with this self-adaption strategy (named Algorithm 1) is able to approximately obtain the expected number of non-dominated solutions, which are very close to and uniformly distributed on the Pareto-optimal front. Furthermore, the genetic drift phenomenon in Algorithm 1 is discussed Two cases of genetic drift are pointed out, and one case can be fixed up by a simple approach proposed in this paper.

关键词： Evolutionary computation

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From Wrapping to Knowledge: Domain Ontology Learning from Deep Web

From Wrapping to Knowledge: Domain Ontology Learning from De...

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International Symposiums on Information Processing (ISIP)

作者： Zhiming Cui Pengpeng Zhao Wei Fang Chao Lin The Institute of Intelligent Information Processing and Application China Jiangsu Key Laboratory of Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9781424430536;9780769531519

The next generation of the Web, called Semantic Web, has to improve the Web with semantic page annotations to enable knowledge-level querying and searches. However, manual construction of these ontologies is a time consuming and difficult task. In this paper, we describe an automatic extraction method that learns domain ontologies for semantic web from deep web. Our approach first learns a base ontology from deep web query interfaces, then grows the current ontology by probing the sources and discovering additional concepts and instances from the result pages. We have evaluated our approach in several real-world domains. Preliminary results indicate that the proposed extraction method discovers concepts and instances with high accuracy.

关键词： Wrapping Ontologies Semantic Web Information processing Databases Knowledge engineering Chaos application software Laboratories Machine learning

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Ontology-Based Deep Web Synchronous-Annotation

Ontology-Based Deep Web Synchronous-Annotation

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ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM

作者： PengYu Hu Wei Fang ZhiMing Cui Jiangsu Provincial Key Laboratory of Computer Information Processing Technology The Institute of Intelligent Information Processing and Application Soochow University Suzhou China

An increasing number of databases have become Web accessible through HTML form-based search interfaces, which is so-called deep Web. For full utilization of deep Web resources and improving Web intelligence, which is essential for many applications such as deep Web data collection and comparison shopping, they need to be extracted out and assigned meaningful labels. In this paper, we present a synchronous-annotation approach that introduce domain ontology as a global schema ordered by Web databases to the annotation process. We combine ontology, interface schema and result schema and adopt the strategy of query ontology instance to implement annotation. In order to verify the effectiveness of the method proposed in this paper, we test on a number of different areas of Web databases. The experimental results indicate that the proposed approach is more effective than existing approaches.

关键词： Ontologies Databases Vocabulary Data mining Books Music Motion pictures

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SDWS: Semantic Search for Deep Web Data

SDWS: Semantic Search for Deep Web Data

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International Symposiums on Information Processing (ISIP)

作者： Wei Fang Zhiming Cui Pengyu Hu Li Huang Pengpeng Zhao Jiangsu Key Laboratory of Computer Information Processing Technology Soochow University Suzhou Jiangsu China Institute of Intelligence Information Processing and Application China

ISBN: (纸本)9781424430536;9780769531519

A lot of high quality and wealthy data are hidden in backend database and search engines can not index this page, which is called Deep Web. It is mostly accessible through query interfaces. SDWS, a semantic search engine for Deep Web is presented. We are studying and implementing semantic Web technology to the each process of Deep Web information integrated, and expertise in Deep Web discovering, annotating query results and integrating information. The novel approach promise better access to Deep Web.

关键词： Ontologies Search engines Semantic Web Information processing Portals Deductive databases application software Laboratories Indexes Context-aware services

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STUDY ON THE CONSTRUCTION OF DOMAIN TEXT CLASSIFICATION MODEL WITH THE HELP OF DOMAIN KNOWLEDGE

STUDY ON THE CONSTRUCTION OF DOMAIN TEXT CLASSIFICATION MODE...

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2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

作者： JIAN-YI GUO ZHENG-TAO YU LU HAN ZHI-KUN ZHANG XIANG-YAN MENG CUN-LI MAO The School of Information Engineering and Automation Kunming University of Science and Technology The School of Information Engineering and Automation Kunming University of Science and Technology The Institute of Intelligent Information Processing Computer Technology Application Key Laboratory

Traditional text classification model uses statistical methods to obtain features. But in the aspect of discrimination domain and non-domain text category, domain knowledge relations haven't been taken account of in these methods. A domain text classification model was presented in this paper. This model used the support vector machine learning algorithm, gained domain classification feature words through statistic and union domain words, structured domain classification feature space. With the help of domain knowledge relations, computed relevance between domain concepts, got domain classification feature weight. Finally domain text classification was realized. An experiment in the Yunnan tourism domain was carried on to confirm that domain knowledge relations have a good influence on the domain text classification. The classification accuracy rate has been increased 0.04 than improved TFIDF method.

关键词： Tezt classification Feature selection Domain knowledge Relations

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