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001447556 001__ 1447556
001447556 003__ OCoLC
001447556 005__ 20230310004123.0
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001447556 019__ $$a1330407502
001447556 020__ $$a9789811918797$$q(electronic bk.)
001447556 020__ $$a9811918791$$q(electronic bk.)
001447556 020__ $$z9789811918780
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001447556 0247_ $$a10.1007/978-981-19-1879-7$$2doi
001447556 035__ $$aSP(OCoLC)1330932652
001447556 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dGW5XE$$dYDX$$dEBLCP$$dOCLCF$$dOCLCQ
001447556 049__ $$aISEA
001447556 050_4 $$aQA76.9.D343
001447556 08204 $$a006.3/12$$223/eng/20220627
001447556 1001_ $$aYe, Chen,$$d1985-
001447556 24510 $$aKnowledge discovery from multi-sourced data /$$cChen Ye, Hongzhi Wang, Guojun Dai.
001447556 260__ $$aSingapore :$$bSpringer,$$c2022.
001447556 300__ $$a1 online resource (91 pages)
001447556 336__ $$atext$$btxt$$2rdacontent
001447556 337__ $$acomputer$$bc$$2rdamedia
001447556 338__ $$aonline resource$$bcr$$2rdacarrier
001447556 4901_ $$aSpringerBriefs in Computer Science
001447556 504__ $$aIncludes bibliographical references.
001447556 5050_ $$a1. Introduction -- 2. Functional-dependency-based truth discovery for isomorphic data -- 3. Denial-constraint-based truth discovery for isomorphic data -- 4. Pattern discovery for heterogeneous data -- 5. Deep fact discovery for text data.
001447556 506__ $$aAccess limited to authorized users.
001447556 520__ $$aThis 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 "label" 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 challenges. On the structural level, it is essential to consider the different characteristics of data composition and application scenarios and define the knowledge discovery problem on different occasions. On the algorithm level, the knowledge discovery task needs to consider different levels of information conflicts and design efficient algorithms to mine more valuable information using multiple clues. Existing knowledge discovery methods have defects on both the structural level and the algorithm level, making the knowledge discovery problem far from totally solved
001447556 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 27, 2022).
001447556 650_0 $$aData mining.
001447556 655_0 $$aElectronic books.
001447556 7001_ $$aWang, Hongzhi.
001447556 7001_ $$aDai, Guojun.
001447556 77608 $$iPrint version:$$aYe, Chen.$$tKnowledge Discovery from Multi-Sourced Data.$$dSingapore : Springer, ©2022$$z9789811918780
001447556 830_0 $$aSpringerBriefs in computer science.
001447556 852__ $$bebk
001447556 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-1879-7$$zOnline Access$$91397441.1
001447556 909CO $$ooai:library.usi.edu:1447556$$pGLOBAL_SET
001447556 980__ $$aBIB
001447556 980__ $$aEBOOK
001447556 982__ $$aEbook
001447556 983__ $$aOnline
001447556 994__ $$a92$$bISE