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Heterogeneous deep web data extraction using ontology evolution

Journal of Convergence Information Technology 2010年第8期5卷 23-23页

作者： Chen, Kerui Zhao, Jinchao Zuo, Wanli He, Fengling Chen, Yongheng College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou 450002 China

This paper proposed a complex ontology evolution based method of extracting data, and also completely designed an extraction system, which consists of four important components: Resolver, Extractor, Consolidator and the ontology construction components. The system gives priority to the construction of mini-ontology. When the user submits query keywords to the deep web query interface, the returned result will pass through the prior three components;after that, the final execution result will be returned to user in a unified form. This paper adopted an extraction method that is different from the general ontology extraction. More specifically, the ontology used in extraction here is dynamic evolution, which can adapt various data source better. Experimental results proved that this method could effectively extract the data in the query result pages.

关键词： Ontology

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An Efficient Method for Very Large Scale Out-of-Core Terrain Visualization

An Efficient Method for Very Large Scale Out-of-Core Terrain...

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International Conference on Artificial Reality and Telexistence (ICAT)

作者： Huijie Zhang Jigui Sun Haihong Yu Changsong Qi College of Computer Science Northeast Normal University China College of Computer Science and Technology Jilin University China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education China

Large scale terrain visualization with high- resolution has an increasing demand in many research fields. To realize the efficient rendering of terrain, this paper presents an out-of-core terrain visualization method based on multi-resolution storage techniques. In external memory, the terrain data set is subdivided from top to bottom to build a multi-resolution hierarchical structure based on a quad-tree. The hierarchical structure can decimate the elevation data that must be loaded into internal memory. Thus it can improve the efficiency of I/O access greatly. Moreover, in order to implement rapid data retrieval of the real time terrain flyover, an efficient indexing algorithm is proposed, in which those nodes in the hierarchical structure will be divided into several clusters in terms of the similarities of static error and the closed space constraint. In addition, a method for crack-free is also proposed here. The comprehensive experiment conducted on the GTOP30 data set shows that this approach outperforms the Block and the Hierarchy algorithms in the both ways of efficiency and simplification ratio.

关键词： Large-scale systems Clustering algorithms Information retrieval Educational institutions Computer science Data visualization Educational technology Costs Data structures Partitioning algorithms

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A Multi-Agent Deep Reinforcement Learning Method for Fully Noisy Observations

SSRN

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SSRN 2023年

作者： Wang, Kaiyu Zhang, Menglin Qu, Bohao Wang, Xianchang College of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Changchun130012 China School of Artificial Intelligence Jilin University Jilin Changchun130012 China Chengdu Kestrel Artificial Intelligence Institute Chengdu611730 China

Multi-agent reinforcement learning (MARL) algorithms have achieved great breakthroughs in many aspects. The MARL algorithms can learn effective policies in ideal simulation environments. But different from the ideal simulation environments, noise is unavoidable in the real world. MARL algorithms need to learn effective policies in unavoidable fully noisy environments. In this paper, we consider a challenging multi-agent reinforcement learning problem: All agents cannot observe any noiseless observations from environments during the whole training process and MARL algorithms cannot learn effective policies in these fully noisy observation environments. To solve this problem, we propose a method called Robust Policy Learning under Fully Noisy Observation viA DeNoising REpresentation NeTwork (PLANET), which enables MARL algorithms learning effective policies in fully noisy observation environments. The PLANET method learns the effective policy through two steps. (1) Extracting the noise characteristics and motion laws information from fully noisy observation histories. (2) Making MARL algorithms extract information from the noise characteristics and motion laws information and learn effective policies. The results of a series of exhaustive experiments show that our method can eliminate the effects of noise and learn effective policies in fully noisy observation environments. © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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Operon Prediction by Markov Clustering

Operon Prediction by Markov Clustering

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2011·国际基因组学大会

作者： Wei Du Zhongbo Cao Yan Wang Enrico Blanzieri Chen Zhang Yanchun Liang College of Mathematics Jilin University Changchun 130012 China Department of Information and Communication Technology University of Trento Povo 38050 Italy College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China

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Implicit knowledge discovery in biomedical ontologies: Computing interesting relatednesses

Implicit knowledge discovery in biomedical ontologies: Compu...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tian Bai Leiguang Gong Casimir A. Kulikowski Lan Huang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Yantai Intelligent Information Technologies Ltd. Yantai China Department of Computer Science Rutgers The State University of New Jersey Piscataway NJ USA

Ontologies, seen as effective representations for sharing and reusing knowledge, have become increasingly important in biomedicine, usually focusing on taxonomic knowledge specific to a subject. Efforts have been made to uncover implicit knowledge within large biomedical ontologies by exploring semantic similarity and relatedness between concepts. However, much less attention has been paid to another potentially helpful approach: discovering implicit knowledge across multiple ontologies of different types, such as disease ontologies, symptom ontologies, and gene ontologies. In this paper, we propose a unified approach to the problem of ontology based implicit knowledge discovery - a Multi-Ontology Relatedness Model (MORM), which includes the formation of multiple related ontologies, a relatedness network and a formal inference mechanism based on set-theoretic operations. Experiments for biomedical applications have been carried out, and preliminary results show the potential value of the proposed approach for biomedical knowledge discovery.

关键词： Biological system modeling Biomedical measurement Ontologies Arthritis Diseases

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Adaptive and iterative gene selection based on least squares support vector regression

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Journal of Information and computational Science 2006年第3期3卷 443-451页

作者： Jiang, Jingqing Wu, Chunguo Song, Chuyi Liang, Yanchun Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China College of Mathematics and Computer Science Inner Mongolia University for Nationalities Tongliao 028043 China Key Laboratory of Information Science and Engineering of Railway Ministry The Key Laboratory of Advanced Information Science and Network Technology of Beijing Beijing Jiaotong University Beijing 100044 China

Cancer classification and identification are major areas in medical research. DNA microarrays could provide useful information for cancer classification at the gene expression level. The number of genes in a microarray is always several thousands while the number of training samples always several dozens. In such case most of the machine learning models suffer from the overfitting and it is necessary to select a handful of most informative genes. An adaptive and iterative gene selection algorithm based on least squares support vector machines is proposed in this paper. The algorithm adopts sequential forward selection search scheme. The number of selected genes can be determined adaptively. The total number of genes processed by the proposed algorithm is smaller than that processed by other algorithms using support vector machines. Results of numerical experiments show that the proposed algorithm trains fast and achieves comparable performance on two well-known benchmark problems.

关键词： Genes

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IInfoGAN: Improved Information Maximizing Generative Adversarial Networks

IInfoGAN: Improved Information Maximizing Generative Adversa...

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International Conference on Mechanical, Control and Computer engineering (ICMCCE)

作者： Yan Wang Pujia Wang Boyang Sun Kai He Lan Huang School of Artificial Intelligence Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Ministry of Education Changchun China

Generative Adversarial Networks (GANs) have achieved huge success in some unsupervised learning fields. There is no doubt that clustering takes a lot of weight in unsupervised algorithm. And in this paper, we raise the Improved Information Maximizing Generative Adversarial Networks (IInfoGAN) algorithm for learning discriminative classifiers from unlabeled data. The basis of our method is an math function that contains the Mutual Information (MI) and Cross Entropy of the observed examples and their predicted classification category distribution, thus enhancing the robustness of the classifier to adversarial generative models. Experiments show that the interpretable representation learned by IInfoGAN is competitive with the representation learned by existing unsupervised methods.

关键词： Training Time series analysis Clustering algorithms Predictive models Generative adversarial networks Prediction algorithms Entropy

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Hyperbolic node embedding for signed networks

arXiv

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arXiv 2019年

作者： Song, Wenzhuo Chen, Hongxu Liu, Xueyan Jiang, Hongzhe Wang, Shengsheng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Advanced Analytics Institute School of Computer Science Faculty of Engineering and IT University of Technology Sydney NSW2007 Australia College of Mechanical and Electronic Engineering Nanjing Forestry University Nanjing210037 China

Signed network embedding methods aim to learn vector representations of nodes in signed networks. However, existing algorithms only managed to embed networks into low-dimensional Euclidean spaces whereas many intrinsic features of signed networks are reported more suitable for non-Euclidean spaces. For instance, previous works did not consider the hierarchical structures of networks, which is widely witnessed in real-world networks. In this work, we answer an open question that whether the hyperbolic space is a better choice to accommodate signed networks and learn embeddings that can preserve the corresponding special characteristics. We also propose a non-Euclidean signed network embedding method based on structural balance theory and Riemannian optimization, which embeds signed networks into a Poincar ball in a hyperbolic space. This space enables our approach to capture underlying hierarchy of nodes in signed networks because it can be seen as a continuous tree. We empirically compare our method against six Euclidean-based baselines in three tasks on seven real-world datasets, and the results show the effectiveness of our method. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

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Approximated non-tensor pre-wavelets neural network based on compound training algorithm

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Journal of Information and computational Science 2005年第4期2卷 715-722页

作者： Li, Ying Deng, Zhidong Zou, Xi Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 China Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130021 China Institute of Mathematics Jilin University Changchun 130021 China

In this paper, we propose a new supervised compound learning algorithm for training our constructed approximated bivariate non-tensor product adaptive pre-wavelet neural network (APWNN). On the one hand, the linear weights of APWNN are trained by the self-adaptive learning rate method. On the other hand an extended Kalman filter method is used to update the nonlinear parameters such as dilation parameters and translation parameters. Additionally we demonstrate the efficiency of our proposed method through a concrete example of function approximation.

关键词： Neural networks

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Using spatial clustering in precision agriculture

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Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology 2005年第SUPPL. 4期37卷 31-34页

作者： Wang, Sheng-Sheng Liu, Da-You Liu, Jie Wang, Xin-Ying Key Laboratory of Symbolic Computing and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China

There are lots of data with multidimensional attributes and spatial position information in precision agriculture applications. Based on the high dimensional spatial clustering algorithm, the agriculture breed partition method is proposed and applied in Chinese national 863 project. The data mining algorithm is performed in this way: cluster the multidimensional attributes first, then cluster the spatial position information using the former attributes clustering results. Experiments show that the two-phases clustering method is prior to the traditional single-phase clustering method. According to the clustering results, one will decide the region belongs to what kind of soil, select the breeds and the field managements.

关键词： Data mining

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