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Books > Computing & IT > Applications of computing > Artificial intelligence
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Knowledge Discovery from Multi-Sourced Data (Paperback, 1st ed. 2022)
Loot Price: R1,271
Discovery Miles 12 710
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Knowledge Discovery from Multi-Sourced Data (Paperback, 1st ed. 2022)
Series: SpringerBriefs in Computer Science
Expected to ship within 10 - 15 working days
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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.
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