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Titlebook: Web and Big Data; APWeb-WAIM 2019 Inte Jingkuan Song,Xiaofeng Zhu Conference proceedings 2019 Springer Nature Switzerland AG 2019 artificia

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書目名稱Web and Big Data
副標(biāo)題APWeb-WAIM 2019 Inte
編輯Jingkuan Song,Xiaofeng Zhu
視頻videohttp://file.papertrans.cn/1022/1021676/1021676.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Web and Big Data; APWeb-WAIM 2019 Inte Jingkuan Song,Xiaofeng Zhu Conference proceedings 2019 Springer Nature Switzerland AG 2019 artificia
描述.This book constitutes the thoroughly refereed post-conference proceedings of the Third APWeb-WAIM 2019 Workshops, held jointly with the Third International Joint Conference APWeb-WAIM 2019 in Macau, China, in August 2019...The 8 full papers presented together with 1 invited talk were carefully reviewed and selected from 18 submissions. The papers originating from two workshops, KGMA and DSEA, present cutting-edge ideas, results, experiences, techniques, and tools from all aspects of the management, analysis, and application of knowledge graphs in different domains, as well as theoretical foundations, algorithms, systems, models and applications for big data management and mining..
出版日期Conference proceedings 2019
關(guān)鍵詞artificial intelligence; computer network; computer systems; databases; education; image processing; infor
版次1
doihttps://doi.org/10.1007/978-3-030-33982-1
isbn_softcover978-3-030-33981-4
isbn_ebook978-3-030-33982-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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0302-9743 ences, techniques, and tools from all aspects of the management, analysis, and application of knowledge graphs in different domains, as well as theoretical foundations, algorithms, systems, models and applications for big data management and mining..978-3-030-33981-4978-3-030-33982-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Community Detection in Knowledge Graph Network with Matrix Factorization Learningh knowledge graph and semantic network. Comparing with relevant network community detection methods in various semantic network data, our matrix factorization learning method achieves good performance for community detection.
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Classification-Based Emoji Recommendation for User Social Networksfferent context sets according to emojis, then propose a method to generate emoji-related features by analysis, finally a classification-based recommendation method is proposed by integrating these features. The experimental results show that the proposed method significantly improves the accuracy of emoji recommendation in social media platforms.
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Boundary Detector Encoder and Decoder with Soft Attention for Video Captioning input video. Extensive experiments are conducted on two datasets: the Microsoft Video Description Corpus and the MSR-Video To Text. Three metrics, BLEU@4, METEOR and CIDEr are used to evaluate our approach. Experimental results demonstrate the effectiveness of our approach.
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