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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Eric C. C. Tsang Su-Yun Zhao Faculty of Information Technology Macau University of Science and Technology Macau China MOE Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China) Beijing China

ISBN: (纸本)9781509003914

Attribute reduction is an inevitable problem in machine learning and statistical learning. To improve the traditional rough set reduction, statistical rough sets is then proposed by introducing random sampling into the rough approximation. Random sampling is the main contribution of statistical rough sets. As a result, it is necessary to analyze the randomness of statistical rough sets. In this paper, we analyze and demonstrate the influence of the randomness in the process of attribute reduction by a large number of experiments to test the effectiveness and stability of the random sampling.

关键词： Rough sets Standards Cybernetics Fluctuations Stability analysis Big data Algorithm design and analysis

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Measuring Semantic Associaiton in Domain Ontology

Measuring Semantic Associaiton in Domain Ontology

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International Conference on Semantics, knowledge and Grid (SKG)

作者： Xuan Tian Haihua Li Xiaoyong Du School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOESchool of Information Renmin University of China Beijing China

In domain ontology, semantic association (SA) is used to depict the correlation between two concepts. In this paper, we define semantic association degree (SAD) for measuring SA in the domain ontology. We first present a method to measure SAD of two direct related concepts by evaluating the semantic relationship between them, and then give another method to measure SAD of two indirect related concepts though SAD of two directed neighboring concepts. A set of comparison experiments show the benefit of our approaches.

关键词： Ontologies Psychology data mining Diseases Information retrieval Laboratories data engineering knowledge engineering Text categorization History

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Global Top-k aggregate queries based on x-tuple in uncertain database

Global Top-k aggregate queries based on x-tuple in uncertain...

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24th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2010

作者： Liu, Dexi Wan, Changxuan Xiong, Naixue Park, Jong Hyuk Yeoe, Sang-Soo School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China Dept. of Computer Science Georgia State University Atlanta GA United States Dept. of Computer Science and Engineering Seoul National University of Technology Seoul Korea Republic of Division of Computer Engineering Mokwon University Daejeon Korea Republic of

ISBN: (纸本)9780769540191

A Top-k aggregate query, which is a powerful technique when dealing with large quantity of data, ranks groups of tuples by their aggregate values and returns k groups with the highest aggregate values. However, compared to Top-k in traditional databases, queries over uncertain database are more complicated because of the existence of exponential possible worlds. As a powerful semantic of Top-k in uncertain database, Global Top-k return k highest-ranked tuples according to their probabilities of being in the Top-k answers in possible worlds. We propose a x-tuple based method to process Global Top-k aggregate queries in uncertain database. Our method has two levels, group state generation and G-x-Top-k query processing. In the former level, group states, which satisfy the properties of x-tuple, are generated one after the other according to their aggregate values, while in the latter level, dynamic programming based Global x-tuple Top-k query processing are employed to return the answers. Comprehensive experiments on different data sets demonstrate the effectiveness of the proposed solutions. © 2010 IEEE.

关键词： Dynamic programming

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Subject Event Extraction from Chinese Court Verdict Case via Frame-filling

Subject Event Extraction from Chinese Court Verdict Case via...

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IEEE International Conference on Big knowledge (ICBK)

作者： Gongqing Wu Shengjie Hu Yinghuan Wang Zan Zhang Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

At present, the query and acquisition of the fragmented knowledge in Chinese court verdicts mainly adopt the class case retrieval method based on the search engine and the rough extraction method for a part of the data in court verdicts. These traditional methods cannot structurally extract fragmented knowledge in Chinese court verdicts and meet the needs of people for the follow-up analysis of court verdicts. Thus, in this paper, we present a structured subject event extraction method (SEE) for Chinese court verdict cases combining with techniques of event extraction (EE) and attribute-value pair extraction (AVPE). Specifically, we provide a subject event representation frame for organizing fragmented knowledge in Chinese court verdict cases. Then, we extract subject events from the unstructured cases based on the trained sequence labeling models and constructed heuristic rules, and fill them into the subject event representation frame in the form of attribute-value pairs (AVPs). The experimental results show that SEE can efficiently and automatically extract subject events from Chinese court verdict cases and visually display them via frame-filling, which promotes the efficiency of people in searching for legal materials and facilitates further research and analysis.

关键词： Feature extraction data mining Labeling Information retrieval Semantics Machine learning knowledge engineering

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Interference and power constrained Broadcasting and Multicasting in wireless ad hoc networks with directional antennas

Interference and power constrained Broadcasting and Multicas...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Zheng Li Deying Li Ming Liu Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China China School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China

ISBN: (纸本)9781424451142

Broadcasting/Multicasting problems have been well studied in wireless ad hoc networks. However, only a few approaches take into account the low interference and energy efficiency as the optimization objective simultaneously. In this paper, we study the interference and power constrained broadcast/multicast and the delay-bounded interference and power constrained broadcast/multicast routing problems in wireless ad hoc networks using directional antennas. We propose an approximation and a heuristic algorithm for the two problems, respectively. Importantly, motivated by the study of above optimization problems, we propose approximation schemes for two multi-constrained directed Steiner tree problems, respectively. Broadcast/Multicast message by using the trees found by our algorithms tend to have less channel collisions and higher network throughput. The theoretical results are corroborated by simulation studies.

关键词： Interference constraints Broadcasting Mobile ad hoc networks Directional antennas Multicast algorithms Energy efficiency Routing Approximation algorithms Heuristic algorithms Throughput

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A key-phrase Extraction Method Based on Multi-size Convolution Windows for Scientific Literatures

A Key-phrase Extraction Method Based on Multi-size Convoluti...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Yuhong Zhang Yuxin Xie Peipei Li Xuegang Hu Ministry of Education Ministry of Education Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Hefei Hefei University of Technology Hefei China

ISBN: (纸本)9781665441506

The key-phrase extraction is important for the downstream tasks in natural language process, and has attracted a lot of attention. Compared with other documents, scientific literatures contain many long phrases. Most existing methods perform poor on these literatures. To address this problem, a key-phrase extraction method based on multi-size convolution windows (KE-MCW) is proposed for scientific literatures in this paper. More specifically, in order to represent more contextual information, a convolutional neural network(CNN) with multi-size filters is introduced to map the documents into distributed feature vectors, then each vector can represent different size phrases. Next, in order to determine whether each word is a part of a keyphrase, a deep recurrent neural network is used to mark the role of each word. Finally, the attention mechanism is used to further judge the importance of each phrase. Experimental results show that our proposed method performs better than some competitive methods for technology literatures.

关键词： Cloud computing Recurrent neural networks Convolution Conferences Natural languages Information filters Feature extraction

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The Design of an Open Hybrid Recommendation System for Mobile Commerce

The Design of an Open Hybrid Recommendation System for Mobil...

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International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce

作者： Chengzhi Liu Caihong Sun Jia Yu Information School and Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing China School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia

To meet more and more complex recommendation needs, it is quite important to implement hybrid recommendations for mobile commerce. In this paper, we propose a design for open hybrid recommendation systems in mobile commerce, which could integrate multiple recommendation algorithms together to improve recommendation performance. First, three solutions for an open hybrid recommendation approach are discussed in detail, which are generic customer profile, weighted hybrid recommendation algorithm, and mobile device profile creation. After that, we give out a multi-agent architecture design to make the three solutions work together. Finally a prototype system based on our proposed architecture is implemented to demonstrate the feasibility of our design and evaluate the performance of the proposed open hybrid recommendation system.

关键词： Business Customer profiles Context awareness Laboratories data engineering knowledge engineering Prototypes XML Sun Australia

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Journal of Convergence Information Technology 2012年第16期7卷 87-96页

作者： Minjuan, Zhong School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang 330013 China

With the increasing of XML data over the Internet, managing and analyzing huge amount of XML documents has played an important role for information management. Clustering as an intelligent technique has been utilized as an excellent way of grouping the documents by their content or structure. However, the key problem is how to measure similarity between XML documents. In this paper, we propose an extended vector space model and on this basis put forward an effective semantic similarity measurement method combining content and structure semantics, in which a variety of XML document features impacting similarity measurement, such as term element frequency, term inverse element frequency, semantic weight of tag and level information of the term, are analyzed. In addition, information gain, for clustering quality evaluation are introduced motivated by the fact that collection has no classification information in advance. Experiment results show that proposed similarity method (EVSM_SS) outperforms the content and structure integration measurement based on structure path (VSM_SP) as well as traditional document clustering measurement (CO) in information gain and produce better clustering quality.

关键词： XML

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Combining term semantics with content and structure semantics for XML element search results clustering

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Journal of Convergence Information Technology 2012年第15期7卷 26-35页

The biggest characteristic of the XML retrieval is able to return the element node results. This paper studies XML element search results clustering and proposes one similarity measurement method based on term semantics, in which the "core" concept between terms is got through latent semantic indexing technology(LSI) and the same time the XML element node content and semantic structure properties(CASS) are combined. In addition, two new performance evaluation methodologies, namely R_ClusterRatio and R_DocuRatio are introduced to evaluate clustering quality. It is motivated by the observations of relevant documents distribution and the fact that the experiment data collection, IEEE CS corpus, do not provide classification information. Experiment results show that proposed similarity method combining term semantics with content and structure semantics integration(LSI-CASS) is feasible, and it produces better clustering quality than LSI-CAS and CASS.

关键词： Semantics

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Clustering XML retrieval results based on hybrid similarity

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Journal of Computational Information Systems 2008年第3期4卷 1323-1330页

作者： Wan, Changxuan Yu, Hong School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang 330013 China

With the unceasing growth of XML data in World Wide Web, XML document retrieval and clustering retrieval results are confronted with both challenges and opportunities. One of the challenges is how to improve the quality of XML retrieval results. Firstly, according to the features of XML documents, a method of modeling XML retrieval result documents is brought forward, which integrates both structural semantic features and content information of XML documents. Then, a measure method to compute similarity, including structural semantic similarity and keywords similarity, between retrieval result documents is suggested;and a strategy named Item Frequency in Cluster-Inverse Cluster Frequency to extract labels from result clusters is presented. Experiments indicate that the clustering quality for XML retrieval results based on hybrid similarity is obviously better than the one only based on content similarity.

关键词： XML

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