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Learning object identification rules...

Tejada, Sheila Ann.

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Learning object identification rules for information integration.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning object identification rules for information integration./
Author:	Tejada, Sheila Ann.
Description:	108 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4469.
Contained By:	Dissertation Abstracts International64-09B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3103973
ISBN:	0496515411

Learning object identification rules for information integration.
Tejada, Sheila Ann.

Learning object identification rules for information integration. - 108 p.

Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4469.

Thesis (Ph.D.)--University of Southern California, 2003.

When integrating information from multiple websites, the same data objects can exist in inconsistent text formats across sites, making it difficult to identify matching objects using exact text match. We have developed an object identification system called Active Atlas, which compares the objects' shared attributes in order to identify matching objects. Certain attributes are more important for deciding if a mapping should exist between two objects. Previous methods of object identification have required manual construction of object identification rules or mapping rules for determining the mappings between objects, as well as domain-dependent transformations for recognizing format inconsistencies. This manual process is time consuming and error-prone. In our approach, Active Atlas learns to simultaneously tailor both mapping rules and a set of general transformations to a specific application domain, through limited user input. The experimental results demonstrate that we achieve higher accuracy and require less user involvement than previous methods across various application domains.

ISBN: 0496515411Subjects--Topical Terms:

626642
Computer Science.

Learning object identification rules for information integration.
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035 $a (UnM)AAI3103973
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040 $a UnM $c UnM
100 1 $a Tejada, Sheila Ann. $3 1936180
245 1 0 $a Learning object identification rules for information integration.
300 $a 108 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4469.
500 $a Adviser: Craig A. Knoblock.
502 $a Thesis (Ph.D.)--University of Southern California, 2003.
520 $a When integrating information from multiple websites, the same data objects can exist in inconsistent text formats across sites, making it difficult to identify matching objects using exact text match. We have developed an object identification system called Active Atlas, which compares the objects' shared attributes in order to identify matching objects. Certain attributes are more important for deciding if a mapping should exist between two objects. Previous methods of object identification have required manual construction of object identification rules or mapping rules for determining the mappings between objects, as well as domain-dependent transformations for recognizing format inconsistencies. This manual process is time consuming and error-prone. In our approach, Active Atlas learns to simultaneously tailor both mapping rules and a set of general transformations to a specific application domain, through limited user input. The experimental results demonstrate that we achieve higher accuracy and require less user involvement than previous methods across various application domains.
590 $a School code: 0208.
650 4 $a Computer Science. $3 626642
650 4 $a Artificial Intelligence. $3 769149
690 $a 0984
690 $a 0800
710 2 0 $a University of Southern California. $3 700129
773 0 $t Dissertation Abstracts International $g 64-09B.
790 1 0 $a Knoblock, Craig A., $e advisor
790 $a 0208
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3103973