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Titlebook: Linked Data and Knowledge Graph; Seventh Chinese Sema Guilin Qi,Jie Tang,Yong Yu Conference proceedings 2013 The Editor(s) (if applicable)

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书目名称Linked Data and Knowledge Graph
副标题Seventh Chinese Sema
编辑Guilin Qi,Jie Tang,Yong Yu
视频videohttp://file.papertrans.cn/587/586754/586754.mp4
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Linked Data and Knowledge Graph; Seventh Chinese Sema Guilin Qi,Jie Tang,Yong Yu Conference proceedings 2013 The Editor(s) (if applicable)
描述This book constitutes the refereed proceedings of the Seventh Chinese Semantic Web Symposium and the Second Chinese Web Science Conference, CSWS 2013, held in Shanghai, China in August 2013. The 14 revised full papers presented together with 6 short papers and 3 poster papers were carefully reviewed and selected from 48 submissions. The papers cover a wide range of topics, including semantic search, ontology reasoning, social semantic Web, knowledge graph, etc.
出版日期Conference proceedings 2013
关键词Automated reasoning; Knowledge representation; Semantic mining; Semantic relations; Web ontology
版次1
doihttps://doi.org/10.1007/978-3-642-54025-7
isbn_softcover978-3-642-54024-0
isbn_ebook978-3-642-54025-7Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer-Verlag GmbH, DE
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1865-0929 CSWS 2013, held in Shanghai, China in August 2013. The 14 revised full papers presented together with 6 short papers and 3 poster papers were carefully reviewed and selected from 48 submissions. The papers cover a wide range of topics, including semantic search, ontology reasoning, social semantic W
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Searching Semantic Associations Based on Virtual Document,idea. To characterize the semantics of each association, a virtual document of each association is built with the help of a neighboring operation. A searching model of virtual documents of associations and a ranking schema are also discussed in this paper. Experiments show that our approach is feasible and efficient.
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Evaluating Article Quality and Editor Reputation in Wikipedia, an efficient algorithm. We conduct experiments to validate the effectiveness of the proposed model. By leveraging the belief propagation between articles and editors, our approach obtains significant improvement over several alternative methods(SVM, LR, PR, CRF).
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