國立虎尾科技大學 |

Human-Centric Debugging of Entity Matching.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Human-Centric Debugging of Entity Matching./
Author:	Panahi, Fatemah.
Description:	1 online resource (151 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-06(E), Section: B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781369529838

Human-Centric Debugging of Entity Matching.
Panahi, Fatemah.

Human-Centric Debugging of Entity Matching. - 1 online resource (151 pages)

Source: Dissertation Abstracts International, Volume: 78-06(E), Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2017.

Includes bibliographical references

Entity matching (EM) is the problem of finding data records that refer to the same real-world entity. For example, the two records (Matthew Richardson, 206-453-1978) and (Matt W. Richardson, 453 1978) may refer to the same person. It is an important data integration problem with many applications such as in e-commerce, healthcare, and national security. Recent work on entity matching has focused on using machine learning and/or crowdsourcing in order to improve accuracy and/or scale the current matching solutions despite the fact that this task is typically done with a human analyst in the loop. Therefore, in this thesis we propose to work on solutions that acknowledge that humans are in the loop for completing an entity matching task. We focus on debugging of entity matching, which is an iterative process by which an analyst improves matching quality. Hence the title, "Human-Centric Debugging of Entity Matching''.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369529838Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Human-Centric Debugging of Entity Matching.
LDR:03098ntm a2200325K 4500 001 913887
005 20180628100930.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9781369529838
035 $a (MiAaPQ)AAI10254754
035 $a (MiAaPQ)wisc:14254
035 $a AAI10254754
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Panahi, Fatemah. $3 1186905
245 1 0 $a Human-Centric Debugging of Entity Matching.
264 0 $c 2017
300 $a 1 online resource (151 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 78-06(E), Section: B.
500 $a Adviser: Jeffrey F. Naughton.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2017.
504 $a Includes bibliographical references
520 $a Entity matching (EM) is the problem of finding data records that refer to the same real-world entity. For example, the two records (Matthew Richardson, 206-453-1978) and (Matt W. Richardson, 453 1978) may refer to the same person. It is an important data integration problem with many applications such as in e-commerce, healthcare, and national security. Recent work on entity matching has focused on using machine learning and/or crowdsourcing in order to improve accuracy and/or scale the current matching solutions despite the fact that this task is typically done with a human analyst in the loop. Therefore, in this thesis we propose to work on solutions that acknowledge that humans are in the loop for completing an entity matching task. We focus on debugging of entity matching, which is an iterative process by which an analyst improves matching quality. Hence the title, "Human-Centric Debugging of Entity Matching''.
520 $a We build an end-to-end matching system and experiment with it in an e-commerce setting as well as with students in a graduate data modeling course at UW-Madison. We also develop an abstract model of the entity matching problem for an analyst to understand what makes an entity matching problem hard for an analyst. The insights learned in the above work lead to the following works in the rest of the thesis: First, we focus on debugging rule-based matchers and we attempt to make it an interactive process by which an analyst can quickly iterate and find a high quality matcher. We show that by optimally ordering the rules as well as incrementally running the matcher on top of previous matching output we can decrease runtime significantly. And second, we focus on debugging of entity matching data sets. We develop a framework to help an analyst quickly find and resolve inconsistencies in a data set. We experiment with seven real-world data sets and demonstrate the effectiveness of our framework in finding inconsistencies.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Wisconsin - Madison. $b Computer Sciences. $3 1179878
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10254754 $z click for full text (PQDT)