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Titlebook: Knowledge Discovery from Multi-Sourced Data; Chen Ye,Hongzhi Wang,Guojun Dai Book 2022 The Author(s), under exclusive license to Springer

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發(fā)表于 2025-3-21 18:42:38 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Knowledge Discovery from Multi-Sourced Data
編輯Chen Ye,Hongzhi Wang,Guojun Dai
視頻videohttp://file.papertrans.cn/544/543865/543865.mp4
概述Provides various techniques to discover useful knowledge based on different data models of multi-sourced data.Covers both truth discovery and fact discovery based on different data quality properties
叢書名稱SpringerBriefs in Computer Science
圖書封面Titlebook: Knowledge Discovery from Multi-Sourced Data;  Chen Ye,Hongzhi Wang,Guojun Dai Book 2022 The Author(s), under exclusive license to Springer
描述This book addresses several knowledge discovery problems on multi-sourced data where the theories, techniques, and methods in data cleaning, data mining, and natural language processing are synthetically used. This book mainly focuses on three data models: the multi-sourced isomorphic data, the multi-sourced heterogeneous data, and the text data. On the basis of three data models, this book studies the knowledge discovery problems including truth discovery and fact discovery on multi-sourced data from four important properties: relevance, inconsistency, sparseness, and heterogeneity, which is useful for specialists as well as graduate students..?.Data, even describing the same object or event, can come from a variety of sources such as crowd workers and social media users. However, noisy pieces of data or information are unavoidable. Facing the daunting scale of data, it is unrealistic to expect humans to “l(fā)abel” or tell which data source is more reliable.Hence, it is crucial to identify trustworthy information from multiple noisy information sources, referring to the task of knowledge discovery..?.At present, the knowledge discovery research for multi-sourced data mainly faces two
出版日期Book 2022
關(guān)鍵詞Truth Discovery; Source Reliability; Integrity Constraints; Optimization Framework; Fact Extraction; Data
版次1
doihttps://doi.org/10.1007/978-981-19-1879-7
isbn_softcover978-981-19-1878-0
isbn_ebook978-981-19-1879-7Series ISSN 2191-5768 Series E-ISSN 2191-5776
issn_series 2191-5768
copyrightThe Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
The information of publication is updating

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https://doi.org/10.1007/978-981-19-1879-7Truth Discovery; Source Reliability; Integrity Constraints; Optimization Framework; Fact Extraction; Data
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SpringerBriefs in Computer Sciencehttp://image.papertrans.cn/k/image/543865.jpg
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Introduction,ckground of knowledge discovery from multi-source data. In Sect.?., we analyze the multi-source data quality to motivate the necessity of discovering useful information from noisy sources. In Sect.?., we summarize the existing studies and explore the drawbacks. We conclude the chapter with an overvi
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Book 2022o expect humans to “l(fā)abel” or tell which data source is more reliable.Hence, it is crucial to identify trustworthy information from multiple noisy information sources, referring to the task of knowledge discovery..?.At present, the knowledge discovery research for multi-sourced data mainly faces two
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2191-5768 to identify trustworthy information from multiple noisy information sources, referring to the task of knowledge discovery..?.At present, the knowledge discovery research for multi-sourced data mainly faces two978-981-19-1878-0978-981-19-1879-7Series ISSN 2191-5768 Series E-ISSN 2191-5776
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