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Titlebook: Computational Intelligence for Semantic Knowledge Management; New Perspectives for Giovanni Acampora,Witold Pedrycz,Autilia Vitiello Book 2

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發(fā)表于 2025-3-21 18:51:32 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Computational Intelligence for Semantic Knowledge Management
副標(biāo)題New Perspectives for
編輯Giovanni Acampora,Witold Pedrycz,Autilia Vitiello
視頻videohttp://file.papertrans.cn/233/232461/232461.mp4
概述Provides a comprehensive overview of computational intelligence methods for semantic knowledge management.Covers both theoretical and application aspects.Written by leading experts in the field
叢書(shū)名稱(chēng)Studies in Computational Intelligence
圖書(shū)封面Titlebook: Computational Intelligence for Semantic Knowledge Management; New Perspectives for Giovanni Acampora,Witold Pedrycz,Autilia Vitiello Book 2
描述This book provides a comprehensive overview of computational intelligence methods for semantic knowledge management. Contrary to popular belief, the methods for semantic management of information were created several decades ago, long before the birth of the Internet. In fact, it was back in 1945 when Vannevar Bush introduced the idea for the first protohypertext: the MEMEX (MEMory + indEX) machine. In the years that followed, Bush’s idea influenced the development of early hypertext systems until, in the 1980s, Tim Berners Lee developed the idea of the World Wide Web (WWW) as it is known today. From then on, there was an exponential growth in research and industrial activities related to the semantic management of the information and its exploitation in different application domains, such as healthcare, e-learning and energy management..?.However, semantics methods are not yet able to address some of the problems that naturally characterize knowledge management, such as the vagueness and uncertainty of information. This book reveals how computational intelligence methodologies, due to their natural inclination to deal with imprecision and partial truth, are opening new positive sc
出版日期Book 2020
關(guān)鍵詞Computational Intelligence; Evolutionary Computation; Fuzzy Systems; Neural Networks; Ontologies; Ontolog
版次1
doihttps://doi.org/10.1007/978-3-030-23760-8
isbn_ebook978-3-030-23760-8Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Empirical Evidence on Long-Term Competition,ted to the user profiles. These models are conceived for individual and group recommendation scenarios respectively, as a data preprocessing step before the recommendation generation. Two case studies are developed to show that the proposals lead to improvements in the accuracy of individual and group recommender systems.
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Empirical Evidence on Long-Term Competition,aches, by using collaborative filtering techniques and semantic tagging, in order to rank mashups based on user goals. We have proven the validity of the proposed approach through experimental sessions based on data from the ProgrammableWeb repository.
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