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Titlebook: Knowledge Science, Engineering and Management; 11th International C Weiru Liu,Fausto Giunchiglia,Bo Yang Conference proceedings 2018 Spring

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发表于 2025-3-21 16:58:53 | 显示全部楼层 |阅读模式
书目名称Knowledge Science, Engineering and Management
副标题11th International C
编辑Weiru Liu,Fausto Giunchiglia,Bo Yang
视频videohttp://file.papertrans.cn/545/544054/544054.mp4
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Knowledge Science, Engineering and Management; 11th International C Weiru Liu,Fausto Giunchiglia,Bo Yang Conference proceedings 2018 Spring
描述.This two volume set of LNAI 11061 and LNAI 11062 constitutes the refereed proceedings of the 11th International Conference on Knowledge Science, Engineering and Management, KSEM 2018, held in Changchun, China, in August 2018...The 62 revised full papers and 26 short papers presented were carefully reviewed and selected from 262 submissions. The papers of the first volume are organized in the following topical sections: text mining and document analysis; image and video data analysis; data processing and data mining; recommendation algorithms and systems; probabilistic models and applications; knowledge engineering applications; and knowledge graph and knowledge management. The papers of the second volume are organized in the following topical sections: constraints and satisfiability; formal reasoning and ontologies; deep learning; network knowledge representation and learning; and social knowledge analysis and management..
出版日期Conference proceedings 2018
关键词artificial intelligence; classification; classification accuracy; data mining; image processing; informat
版次1
doihttps://doi.org/10.1007/978-3-319-99365-2
isbn_softcover978-3-319-99364-5
isbn_ebook978-3-319-99365-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

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发表于 2025-3-21 21:42:41 | 显示全部楼层
Knowledge Science, Engineering and Management978-3-319-99365-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
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发表于 2025-3-22 04:01:41 | 显示全部楼层
https://doi.org/10.1007/978-3-319-99365-2artificial intelligence; classification; classification accuracy; data mining; image processing; informat
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A Biomedical Question Answering System Based on SNOMED-CTxibility and accuracy of the system. The experimental results show that the overall performance of the system has reached a high level, which can give 85% of the correct answer and be used as a biomedical question answering system in a real environment.
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WalkToTopics: Inferring Topic Relations from a Feature Learning Perspectives. Moreover, WalkToTopics is a general model that also can work on exploring topic clusters or extracting sentiments, and can be applied to potential applications, such as ideas tracking and opinion summarization. Finally, we conducted two studies for common users and experts which both quantitative
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TCMEF: A TCM Entity Filter Using Less Textaike and HudongBaike (the two largest Chinese encyclopedia websites) as the main data sources. TCMEF gets an F1 score of 0.9275 in classification, which outperforms general word based short text classification algorithms and is close to a Latent Dirichlet Allocation based model (LDA-SVM) using rich
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