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Titlebook: Web Information Systems and Mining; International Confer Zhiguo Gong,Xiangfeng Luo,Fu Lee Wang Conference proceedings 2011 Springer-Verlag

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發(fā)表于 2025-3-27 00:21:42 | 只看該作者
The Research and Implementation of Heterogeneous Data Integration under Ontology Mapping Mechanismogy and local ontology are constructed as overall data view and local data view respectively. By establishing the mapping between domain ontology and local ontology, the problem of semantic heterogeneity is settled and semantic standard is reached. In this subject the way of automatic mapping is use
32#
發(fā)表于 2025-3-27 02:38:29 | 只看該作者
33#
發(fā)表于 2025-3-27 08:07:26 | 只看該作者
Extracting Hyponymy Patterns in Tibetan Language to Enrich Minority Languages Knowledge Base process in language processing, which is applied in information retrieval, information extraction and automatic translation. Hyponymy pattern is a basic semantic relationship between concepts, which is used to concepts acquisition to enrich ontology automatically. In this paper, the construction id
34#
發(fā)表于 2025-3-27 10:37:03 | 只看該作者
Discovering Atypical Property Values for Object Searches on the Websts when they want to obtain atypical objects or unique information. In these instances, it is difficult for users to expand their queries to match their niche requests. In this paper, we introduce a query suggestion method for finding objects that have atypical characteristics. Our method focuses o
35#
發(fā)表于 2025-3-27 14:09:22 | 只看該作者
36#
發(fā)表于 2025-3-27 18:00:22 | 只看該作者
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發(fā)表于 2025-3-27 23:39:40 | 只看該作者
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發(fā)表于 2025-3-28 05:06:20 | 只看該作者
39#
發(fā)表于 2025-3-28 07:40:38 | 只看該作者
40#
發(fā)表于 2025-3-28 11:22:13 | 只看該作者
Multi-aspect Blog Sentiment Analysis Based on LDA Topic Model and Hownet Lexiconher objects. Usually a Blog page includes more than one theme. However the existing researches of multi-aspect sentiment analysis focus on the product reviews. In this paper, we propose a multi-aspect Chinese Blog sentiment analysis method based on LDA topic model and Hownet lexicon. At first, we us
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