소장자료
LDR | 04045nam a22005535i 4500 | ||
001 | 0100775406▲ | ||
003 | DE-He213▲ | ||
005 | 20231018103457▲ | ||
007 | cr nn 008mamaa▲ | ||
008 | 220613s2022 si | s |||| 0|eng d▲ | ||
020 | ▼a9789811918797▼9978-981-19-1879-7▲ | ||
024 | 7 | ▼a10.1007/978-981-19-1879-7▼2doi▲ | |
050 | 4 | ▼aQA76.9.D343▲ | |
082 | 0 | 4 | ▼a006.312▼223▲ |
100 | 1 | ▼aYe, Chen.▼eauthor.▼4aut▼4http://id.loc.gov/vocabulary/relators/aut▲ | |
245 | 1 | 0 | ▼aKnowledge Discovery from Multi-Sourced Data▼h[electronic resource] /▼cby Chen Ye, Hongzhi Wang, Guojun Dai.▲ |
250 | ▼a1st ed. 2022.▲ | ||
264 | 1 | ▼aSingapore :▼bSpringer Nature Singapore :▼bImprint: Springer,▼c2022.▲ | |
300 | ▼aXII, 83 p. 14 illus., 9 illus. in color.▼bonline resource.▲ | ||
336 | ▼atext▼btxt▼2rdacontent▲ | ||
337 | ▼acomputer▼bc▼2rdamedia▲ | ||
338 | ▼aonline resource▼bcr▼2rdacarrier▲ | ||
347 | ▼atext file▼bPDF▼2rda▲ | ||
490 | 1 | ▼aSpringerBriefs in Computer Science,▼x2191-5776▲ | |
505 | 0 | ▼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.▲ | |
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.▲ | ||
650 | 0 | ▼aData mining.▲ | |
650 | 0 | ▼aDatabase management.▲ | |
650 | 0 | ▼aArtificial intelligence—Data processing.▲ | |
650 | 1 | 4 | ▼aData Mining and Knowledge Discovery.▲ |
650 | 2 | 4 | ▼aDatabase Management.▲ |
650 | 2 | 4 | ▼aData Science.▲ |
700 | 1 | ▼aWang, Hongzhi.▼eauthor.▼4aut▼4http://id.loc.gov/vocabulary/relators/aut▲ | |
700 | 1 | ▼aDai, Guojun.▼eauthor.▼4aut▼4http://id.loc.gov/vocabulary/relators/aut▲ | |
710 | 2 | ▼aSpringerLink (Online service)▲ | |
773 | 0 | ▼tSpringer Nature eBook▲ | |
776 | 0 | 8 | ▼iPrinted edition:▼z9789811918780▲ |
776 | 0 | 8 | ▼iPrinted edition:▼z9789811918803▲ |
830 | 0 | ▼aSpringerBriefs in Computer Science,▼x2191-5776▲ | |
856 | 4 | 0 | ▼uhttps://doi.org/10.1007/978-981-19-1879-7▲ |
Knowledge Discovery from Multi-Sourced Data[electronic resource]
자료유형
국외eBook
서명/책임사항
Knowledge Discovery from Multi-Sourced Data [electronic resource] / by Chen Ye, Hongzhi Wang, Guojun Dai.
판사항
1st ed. 2022.
형태사항
XII, 83 p. 14 illus., 9 illus. in color. online resource.
총서사항
내용주기
1. 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.
요약주기
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 “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.
주제
ISBN
9789811918797
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