Knowledge discovery from multi-sourced data

by Ye Chen

Type Book

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Title Statements

Full title :Knowledge discovery from multi-sourced data / Chen Ye, Hongzhi Wang, Guojun Dai.

Edition Statements

Publication Date:2022

Publication Statements

Publisher(s):Springer
Place of Publication:Singapore
Language:eng
Publication Date:2022

The Physical Object

Description:xii, 83 pages : : illustrations (some color) ; : 24 cm.

Edition Identifiers

ISBN:9811918783, 9789811918780
oclc:on1303063143
LC Classification:QA76.9.D343
Dewey Decimal:006.3/12

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. Fact discovery for text data.

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. Henc…