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Reasoning theory for D3L with compositional bridge rules 1

引用

7th IFIP International Conference on intelligent information processing, IIP 2012

作者： Zhao, Xiaofei Tian, Dongping Chen, Limin Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Research Institute of China Unicom Beijing China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

The semantic mapping in Distributed Dynamic Description Logics (D3L) allows knowledge to propagate from one ontology to another. The current research for knowledge propagation in D3L is only for a simplified case when only two ontologies are involved. In this paper we study knowledge propagation in more complex cases. We find in the case when more than two ontologies are involved and bridge rules form chains, knowledge does not always propagate along chains of bridge rules even if we would expect it. Inspired by Package-based description Logics, we extend the original semantics of D3L by imposing so called compositional consistency condition on domain relations in D3L interpretations. Under this semantics knowledge propagates along chains of bridge rules correctly. Furthermore we provide a distributed Tableaux reasoning algorithm for deciding satisfiability of concepts which is decidable in D3L under compositional consistency. Compared with original one, the extended D3L provides more reasonable logic foundation for distributed, dynamic system such as the information integration system and the Semantic Web. © 2012 IFIP International Federation for information processing.

关键词： Ontology

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Estimate unlabeled-data-distribution for semi-supervised PU learning

引用

14th Asia Pacific Web Technology Conference, APWeb 2012

作者： Hu, Haoji Sha, Chaofeng Wang, Xiaoling Zhou, Aoying Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (纸本)9783642292521

Traditional supervised classifiers use only labeled data (features/label pairs) as the training set, while the unlabeled data is used as the testing set. In practice, it is often the case that the labeled data is hard to obtain and the unlabeled data contains the instances that belong to the predefined class beyond the labeled data categories. This problem has been widely studied in recent years and the semi-supervised learning is an efficient solution to learn from positive and unlabeled examples(or PU learning). Among all the semi-supervised PU learning methods, it's hard to choose just one approach to fit all unlabeled data distribution. This paper proposes an automatic KL-divergence based semi-supervised learning method by using unlabeled data distribution knowledge. Meanwhile, a new framework is designed to integrate different semi-supervised PU learning algorithms in order to take advantage of the former methods. The experimental results show that (1)data distribution information is very helpful for the semi-supervised PU learning method;(2)the proposed framework can achieve higher precision when compared with the-state-of-the-art method. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Learning algorithms

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Ensemble of k-labelset classifiers for multi-label image classification

Ensemble of k-labelset classifiers for multi-label image cla...

引用

7th IFIP International Conference on intelligent information processing, IIP 2012

作者： Zhang, Dapeng Liu, Xi Institute of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783642328909

In the real world, images always have several visual objects instead of only one, which makes it difficult for traditional object recognition methods to deal with them. In this paper, we propose an ensemble method for multi-label image classification. First, we construct an ensemble of k-labelset classifiers. A voting technique is then employed to make predictions for images based on the created ensemble of k-labelset classifiers. We evaluate our method on Corel dataset and demonstrate the precision, recall and F1 measure superior to the state-of-the-art methods. © 2012 IFIP International Federation for information processing.

关键词： Image classification

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Towards Collecting Commonsense Knowledge with Hint Questions

Towards Collecting Commonsense Knowledge with Hint Questions

引用

第8届语义知识与网络国际会议(2012 Eighth International Conference on Semanties,Knowledge and Grids )(SKG2012)

作者： Liangjun Zang Cong Cao Cungen Cao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Aca Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Aca

Accumulating commonsense knowledge (CK) has proven very useful for many natural language processing *** far the most reliable way of acquisition is still relying on knowledge contributors to offer ***,knowledge contributors often fail to think of much CK because this knowledge is usually taken for granted by *** this paper,we explore the issue of reminding knowledge contributors by means of hint ***,we investigate what kinds of CK are easily ignored,analyze its reason and suggest possible *** hope to sketch sources of the research problem and suggest possible ways forward.

关键词： Ontologies History Humans Cognition Computers Roads Reliability

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Improving transfer learning by introspective reasoner 1

引用

7th IFIP International Conference on intelligent information processing, IIP 2012

作者： Shi, Zhongzhi Zhang, Bo Zhuang, Fuzhen Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China Graduate School Chinese Academy of Sciences 100039 Beijing China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

Traditional learning techniques have the assumption that training and test data are drawn from the same data distribution, and thus they are not suitable for dealing with the situation where new unlabeled data are obtained from fast evolving, related but different information sources. This leads to the cross-domain learning problem which targets on adapting the knowledge learned from one or more source domains to target domains. Transfer learning has made a great progress, and a lot of approaches and algorithms are presented. But negative transfer learning will cause trouble in the problem solving, which is difficult to avoid. In this paper we have proposed an introspective reasoner to overcome the negative transfer learning. Introspective learning exploits explicit representations of its own organization and desired behavior to determine when, what, and how to learn in order to improve its own reasoning. According to the transfer learning process we will present the architecture of introspective reasoner for transductive transfer learning. © 2012 IFIP International Federation for information processing.

关键词： Learning systems

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Parallel max-min ant system using mapreduce

Parallel max-min ant system using mapreduce

引用

3rd International Conference on Swarm Intelligence, ICSI 2012

作者： Tan, Qing He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China Graduate University of Chinese Academy of Sciences 100049 Beijing China

ISBN: (纸本)9783642309755

Ant colony optimization algorithms have been successfully applied to solve many problems. However, in some large scale optimization problems involving large amounts of data, the optimization process may take hours or even days to get an excellent solution. Developing parallel optimization algorithms is a common way to tackle with this issue. In this paper, we present a MapReduce Max-Min Ant System (MRMMAS), a MMAS implementation based on the MapReduce parallel programming model. We describe MapReduce and show how MMAS can be naturally adapted and expressed in this model, without explicitly addressing any of the details of parallelization. We present benchmark travelling salesman problems for evaluating MRMMAS. The experimental results demonstrate that the proposed algorithm can scale well and outperform the traditional MMAS with similar running times. © 2012 Springer-Verlag.

关键词： MapReduce

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Support vector machine with mixture of kernels for image classification 1

引用

7th IFIP International Conference on intelligent information processing, IIP 2012

作者： Tian, Dongping Zhao, Xiaofei Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

Image classification is a challenging problem in computer vision. Its performance heavily depends on image features extracted and classifiers to be constructed. In this paper, we present a new support vector machine with mixture of kernels (SVM-MK) for image classification. On the one hand, the combined global and local block-based image features are extracted in order to reflect the intrinsic content of images as complete as possible. SVM-MK, on the other hand, is constructed to shoot for better classification performance. Experimental results on the Berg dataset show that the proposed image feature representation method together with the constructed image classifier, SVMMK, can achieve higher classification accuracy than conventional SVM with any single kernels as well as compare favorably with several state-of-the-art approaches. © 2012 IFIP International Federation for information processing.

关键词： Image classification

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Properties and axiomatization of fuzzy rough sets based on fuzzy covering

Properties and axiomatization of fuzzy rough sets based on f...

引用

2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Wu, Ming-Fen Han, Hao-Han Si, Yan-Fei School of Computer Science Wuyi University Guangdong 529020 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467314855

Some expanded fuzzy rough sets models have been investigated to handle fuzzy databases with uncertain, imprecise and incomplete real-valued information. In this paper, we make further research on fuzzy rough sets models in fuzzy environment, and we generalize rough fuzzy sets model based on a covering to fuzzy rough sets model based on a fuzzy covering. The lower and upper approximations of fuzzy subsets are defined based on a fuzzy covering, and basic properties are investigated. Then, the axiom definition of the lower approximation operator is given. It is shown that the rough fuzzy sets model based on a covering is a special instance of the fuzzy rough sets model based on a fuzzy covering. © 2012 IEEE.

关键词： Fuzzy sets

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Application of FMM combined with higher-order basis functions for electromagnetic scattering problems

Application of FMM combined with higher-order basis function...

引用

2012 International Conference on Microwave and Millimeter Wave Technology, ICMMT 2012

作者： Qi, Qi Chen, Ming-Sheng Wu, Xian-Liang Liu, Yi Ma, Tao Wang, Wang Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei 230039 China School of Electronics and Information Engineering Hefei Normal University Hefei Anhui 230601 China

ISBN: (纸本)9781467321839

In this paper, the fast multipole method (FMM) combined with higher-order basis function method based on the best uniform approximation theory is applied to solve scattering problems of perfectly electrical conduct (PEC). To expand the current by using an appropriate best uniform approximation high-order basis function, then solve the electrical field integral equation (EFIE) using the FMM. It shows in the present examples that the proposed method has higher computational precision than the ordinary FMM in the same subdivision case, this work can provide with some reference value to further research on solving other integral equations. © 2012 IEEE.

关键词： Integral equations

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Quad-tuple PLSA: Incorporating entity and its rating in aspect identification

Quad-tuple PLSA: Incorporating entity and its rating in aspe...

引用

16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012

作者： Luo, Wenjuan Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100039 China

ISBN: (纸本)9783642302169

With the opinion explosion on Web, there are growing research interests in opinion mining. In this study we focus on an important problem in opinion mining - Aspect Identification (AI), which aims to extract aspect terms in entity reviews. Previous PLSA based AI methods exploit the 2-tuples (e.g. the co-occurrence of head and modifier), where each latent topic corresponds to an aspect. Here, we notice that each review is also accompanied by an entity and its overall rating, resulting in quad-tuples joined with the previously mentioned 2-tuples. Believing that the quad-tuples contain more co-occurrence information and thus provide more ability in differentiating topics, we propose a model of Quad-tuple PLSA, which incorporates two more items - entity and its rating, into topic modeling for more accurate aspect identification. The experiments on different numbers of hotel and restaurant reviews show the consistent and significant improvements of the proposed model compared to the 2-tuple PLSA based methods. © 2012 Springer-Verlag.

关键词： Sentiment analysis

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