語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Knowledge discovery from multi-sourc...
~
Ye, Chen.
FindBook
Google Book
Amazon
博客來
Knowledge discovery from multi-sourced data
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Knowledge discovery from multi-sourced data/ by Chen Ye, Hongzhi Wang, Guojun Dai.
作者:
Ye, Chen.
其他作者:
Wang, Hongzhi.
出版者:
Singapore :Springer Nature Singapore : : 2022.,
面頁冊數:
xii, 83 p. :ill., digital ;24 cm.
內容註:
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.
Contained By:
Springer Nature eBook
標題:
Data mining. -
電子資源:
https://doi.org/10.1007/978-981-19-1879-7
ISBN:
9789811918797
Knowledge discovery from multi-sourced data
Ye, Chen.
Knowledge discovery from multi-sourced data
[electronic resource] /by Chen Ye, Hongzhi Wang, Guojun Dai. - Singapore :Springer Nature Singapore :2022. - xii, 83 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..
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.
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.
ISBN: 9789811918797
Standard No.: 10.1007/978-981-19-1879-7doiSubjects--Topical Terms:
562972
Data mining.
LC Class. No.: QA76.9.D343 / Y4 2022
Dewey Class. No.: 006.312
Knowledge discovery from multi-sourced data
LDR
:03171nmm a2200373 a 4500
001
2301799
003
DE-He213
005
20220613221017.0
006
m d
007
cr nn 008maaau
008
230409s2022 si s 0 eng d
020
$a
9789811918797
$q
(electronic bk.)
020
$a
9789811918780
$q
(paper)
024
7
$a
10.1007/978-981-19-1879-7
$2
doi
035
$a
978-981-19-1879-7
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA76.9.D343
$b
Y4 2022
072
7
$a
UNF
$2
bicssc
072
7
$a
UYQE
$2
bicssc
072
7
$a
COM021030
$2
bisacsh
072
7
$a
UNF
$2
thema
072
7
$a
UYQE
$2
thema
082
0 4
$a
006.312
$2
23
090
$a
QA76.9.D343
$b
Y37 2022
100
1
$a
Ye, Chen.
$3
3601534
245
1 0
$a
Knowledge discovery from multi-sourced data
$h
[electronic resource] /
$c
by Chen Ye, Hongzhi Wang, Guojun Dai.
260
$a
Singapore :
$b
Springer Nature Singapore :
$b
Imprint: Springer,
$c
2022.
300
$a
xii, 83 p. :
$b
ill., digital ;
$c
24 cm.
338
$a
online resource
$b
cr
$2
rdacarrier
490
1
$a
SpringerBriefs in computer science,
$x
2191-5776
505
0
$a
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.
520
$a
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.
650
0
$a
Data mining.
$3
562972
650
1 4
$a
Data Mining and Knowledge Discovery.
$3
898250
650
2 4
$a
Database Management.
$3
891010
650
2 4
$a
Data Science.
$3
3538937
700
1
$a
Wang, Hongzhi.
$3
2133338
700
1
$a
Dai, Guojun.
$3
3601535
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer Nature eBook
830
0
$a
SpringerBriefs in computer science.
$3
1567571
856
4 0
$u
https://doi.org/10.1007/978-981-19-1879-7
950
$a
Computer Science (SpringerNature-11645)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9443348
電子資源
11.線上閱覽_V
電子書
EB QA76.9.D343 Y4 2022
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入