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3rd International Conference on Semantics, knowledge, and Grid, SKG 2007

作者： Tian, Xuan Li, HaiHua Du, Xiaoyong School of Information Renmin University of China Beijing 100872 China School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Beijing 100872 China

ISBN: (纸本)0769530079

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. © 2007 IEEE.

关键词： Semantics

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Random analysis of statistical rough set

Random analysis of statistical rough set

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2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

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

ISBN: (纸本)9781509003891

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. © 2016 IEEE.

关键词： Sampling

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Detecting spam comments with malicious users'behavioral characteristics

Detecting spam comments with malicious users'behavioral char...

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2010 IEEE International Conference on Information Theory and Information Security, ICITIS 2010

作者： Wang, Qianqian Liang, Bin Shi, Wenchang Liang, Zhaohui Sun, Wei Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing China Beijing Venustech Cybervision Co. Ltd Beijing 100094 China

ISBN: (纸本)9781424469406

In recent years, the spread of spam comments has become a main obstacle which limits the development of commercialized social networks. This paper analyzes the differences of behavioral characteristics between normal users and malicious users. Based on these characteristics, we propose several heuristic methods to detect spam comments. These methods evaluate comments from three perspectives, including time-frequency characteristic of comments, text similarity of comments and the number of target domains each user's comments refer to. In our collected dataset, our experimental results indicate the accuracy of our detection strategy I (strategy for high accuracy) and strategy II (strategy for wide coverage) are 100% and 92.6%, respectively. The preliminary evaluation of the proposed detection methods shows promising result. © 2010 IEEE.

关键词： Heuristic methods

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RETRACTED ARTICLE: Studies of knowledge management methods from literature

RETRACTED ARTICLE: Studies of knowledge management methods f...

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作者： Zhang, Jing Ye, Liuqi Business School of Hohai University Nanjing Jiangsu 210098 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing 100872 China

ISBN: (纸本)9781424469291

knowledge management methods reflect ways to undertake knowledge management objectives and actions taken for implementation. However, little has been written on the topic. The paper summarizes main topics of knowledge management methods from literature, including research contents, management methods, main types, objects involved, study domains, perspectives, objectives and tasks, school and achieving channels, developing trends and knowledge management methodology and etc. © 2010 IEEE.

关键词： knowledge management

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Services analysis in apparel quotation system based on SOA

Services analysis in apparel quotation system based on SOA

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International Conference on Management and Service Science, MASS 2009

作者： Li, Qian Chen, Yu Lanfang, Zhang Information School and Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9781424446391

Based on the analysis of quote business processes, as well as the characteristics of SOA, a quotation system is brought forward based on SOA for apparel international trade, in order to enhance the Quote's diversity, accuracy as well as efficiency. This paper puts great importance on analyzing how to define, extract, as well as implement services, not only for needs from quotation business process, but also from quotation business goals. Based on the analysis of services, finally, an apparel quotation architecture is described in details. ©2009 IEEE.

关键词： International trade

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A support vector machine based method for credit risk assessment

A support vector machine based method for credit risk assess...

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International Workshop on Cloud Services-Platform Accelerating E-Business

作者： Xu, Wei Zhou, Shenghu Duan, Dongmei Chen, Yanhui Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769542270

The credit card industry has been growing rapidly in recent years, and credit risk assessment becomes critically important for financial companies. In this paper, a novel support vector machine (SVM) based ensemble model is proposed for credit risk assessment. In the proposed method, principles component analysis (PCA) is firstly employed for credit feature selection. Secondly, SVMs with different kernels are trained by using genetic algorithm (GA) to optimize the parameters, and the corresponding assessment results are obtained. Thirdly, all results produced by different SVMs are combined by several ensemble strategies. Finally, an optimal ensemble strategy is selected for credit scoring. For validation, two real world credit datasets are used to test the effectiveness and efficiency of our proposed method. The experiment results find that our proposed ensemble model outperforms commonly used credit scoring tools. The findings of the study reveal the support vector machine based ensemble method to be a promising alternative for credit scoring. © 2010 IEEE.

关键词： Support vector machines

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Towards multi-target search of semantic association 6th

Towards multi-target search of semantic association

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6th Joint International Conference on Semantic Technology, JIST 2016

作者： Zhang, Xiang Lv, Yulian School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Data Engineering and Knowledge Services Nanjing University Nanjing China Southeast University Suzhou China

ISBN: (纸本)9783319501116

Semantic association represents group relationship among objects in linked data. Searching semantic associations is complicated, which involves the search of multiple objects and the search of their group relationships simultaneously. In this paper, we propose this kind of search as a multi-target search, and we compare it to traditional search tasks, which we classify as single-target search. A novel search model is introduced, and the notion of virtual document is used to extract linguistic information of semantic associations. Multi-target search is finally fulfilled by a PageRank-like ranking scheme and a top-K selection policy considering object affinity. Experiments show that our approach is effective in improving retrieval precision on semantic associations. © Springer International Publishing AG 2016.

关键词： Linked data

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The internet of things and personal privacy protection

The internet of things and personal privacy protection

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2010 International Conference of Logistics engineering and Management: Logistics for Sustained Economic Development - Infrastructure, Information, Integration, ICLEM 2010

作者： Zhang, Jing Ye, Liuqi Business School Hohai University Nanjing Jiangsu 210098 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing 100872 China

ISBN: (纸本)9780784411391

Protecting personal privacy and its right are a respect for the human rights, the necessary condition for healthy development of a democratic society and the important criteria of maintaining the basic dignity to be human. The internet of things with its ubiquitous data-aware and intelligent information processing capabilities, improves social efficiency and severe challenges for information security and privacy. In this environment, how to ensure information security and privacy are a major breakthrough in one of the obstacles and an important issue that must be addressed and resolved in the developing process of the internet of things. Thus, in the development of it, we should follow the basic principles of protecting personal privacy such as the entire management, legal compliance, security, adding information, balance and accountability, and establish such models of protecting personal privacy as laws, industry self-regulation and technology to fully protect the inviolability of personal privacy, and remove obstacles and barriers for the applications of the internet of things. © 2010 ASCE.

关键词： Logistics

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Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension 2

Extremely Short Chinese Text Classification Method Based on ...

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2019 2nd International Symposium on Big data and Applied Statistics, ISBDAS 2019

作者： Yue, Yongzeng Zhang, Yuhong Hu, Xuegang Li, Peipei Key Laboratory of Knowledge Engineering with Big Data [Hefei University of Technology] Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei23061 China

Short text classification methods have achieved significant progress and wide application on text data such as Twitter and Weibo. However, the extremely short chinese texts like tax invoice data are different with traditional short texts in lackness of contextual semantic information, feature sparseness and extremely short length. The existing short text classification methods are difficult to achieve a satisfactory performance in these texts. To address these problems, this paper proposes a text classification method based on bidirectional semantic extension for extremely short texts like Chinese tax invoice data. More specifically, firstly, the Chinese knowledge graph is introduced for extending bidirectional semantic of texts and label data to expand the extremely short texts and ease the problem of feature sparseness;secondly, the hash vectorization is used to avoid the semantic problem caused by the lackness of contextual information. Experimental results conducted the real tax invoice dataset demonstrate the effectiveness of our proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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Bootstrap-Based Layerwise Refining for Causal Structure Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2708-2722页

作者： Xiang, Guodu Wang, Hao Yu, Kui Guo, Xianjie Cao, Fuyuan Song, Yukun Hefei University of Technology Key Laboratory of Knowledge Engineering with the Big Data of Ministry of Education Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Shanxi University School of Computer and Information Technology Taiyuan030006 China

Learning causal structures from observational data is critical for causal discovery and many machine learning tasks. Traditional constraint-based methods first adopt conditional independence (CI) tests to learn a global skeleton layer by layer and then orient the undirected edges to obtain a causal structure. However, the reliability of these statistical tests largely depends on the quality of data samples. In real-life scenarios, the presence of data noise or limited samples often makes many CI tests unreliable at each layer in the skeleton learning phase, leading to an inaccurate skeleton. As the number of layers increases, the inaccurate skeleton will continue to impair the skeleton construction of subsequent layers. Furthermore, an unreliable skeleton hampers the skeleton orientation procedure, resulting in an unsatisfactory causal structure. In this article, we propose a Bootstrap-based layerwise refining (BLR) algorithm for causal structure learning, which includes two new procedures to solve the above problems. First, BLR utilizes a novel layerwise skeleton refining procedure to construct the global skeleton layer by layer based on the bootstrap sampling. Second, BLR employs a collective skeleton orientation procedure that incorporates scoring techniques to collectively orient the global skeleton. The experimental results show that BLR outperforms the state-of-the-art methods on the benchmark Bayesian Network datasets. © 2020 IEEE.

关键词： Refining

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