東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Learning to map between structured r...

Doan, AnHai.

Linked to FindBook

Google Book

Amazon

博客來

Learning to map between structured representations of data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning to map between structured representations of data./
Author:	Doan, AnHai.
Description:	159 p.
Notes:	Source: Dissertation Abstracts International, Volume: 63-11, Section: B, page: 5327.
Contained By:	Dissertation Abstracts International63-11B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3072077
ISBN:	0493919120

Learning to map between structured representations of data.
Doan, AnHai.

Learning to map between structured representations of data. - 159 p.

Source: Dissertation Abstracts International, Volume: 63-11, Section: B, page: 5327.

Thesis (Ph.D.)--University of Washington, 2002.

This dissertation studies representation matching: the problem of creating semantic mappings between two data representations. Examples of data representations are relational schemas, ontologies, and XML DTDs. Examples of semantic mappings include " element location of one representation maps to element address of the other ", "contact-phone maps to agent-phone", and "listed-price maps to price * (1 + tax-rate )".

ISBN: 0493919120Subjects--Topical Terms:

626642
Computer Science.

Learning to map between structured representations of data.
LDR:03037nmm 2200301 4500 001 1849351
005 20051206073022.5
008 130614s2002 eng d
020 $a 0493919120
035 $a (UnM)AAI3072077
035 $a AAI3072077
040 $a UnM $c UnM
100 1 $a Doan, AnHai. $3 1640629
245 1 0 $a Learning to map between structured representations of data.
300 $a 159 p.
500 $a Source: Dissertation Abstracts International, Volume: 63-11, Section: B, page: 5327.
500 $a Co-Chairs: Alon Y. Halevy; Pedro M. Domingos.
502 $a Thesis (Ph.D.)--University of Washington, 2002.
520 $a This dissertation studies representation matching: the problem of creating semantic mappings between two data representations. Examples of data representations are relational schemas, ontologies, and XML DTDs. Examples of semantic mappings include " element location of one representation maps to element address of the other ", "contact-phone maps to agent-phone", and "listed-price maps to price * (1 + tax-rate )".
520 $a Representation matching lies at the heart of a broad range of information management applications. Virtually any application that manipulates data in different representation formats must establish semantic mappings between the representations, to ensure interoperability. Prime examples of such applications arise in data integration, data warehousing, data mining, e-commerce, bioinformatics, knowledge-base construction, information processing on the World-Wide Web and on the emerging Semantic Web. Today, representation matching is still mainly conducted by hand, in an extremely labor-intensive and error-prone process. The prohibitive cost of representation matching has now become a key bottleneck hindering the deployment of a wide variety of information management applications.
520 $a In this dissertation we describe solutions for semi-automatically creating semantic mappings. We describe three systems that deal with successively more expressive data representations and mapping classes. Two systems, LSD and GLUE, find one-to-one mappings such as " address = location" in the context of data integration and ontology matching, respectively. The third system, COMAP, finds more complex mappings such as "name = concatenation( first-name,last-name)". The key idea underlying these three systems is the incorporation of multiple types of knowledge and multiple machine learning techniques into all stages of the mapping process, with the goal of maximizing mapping accuracy. I present experiments on real-world data that validate the proposed solutions. Finally, we discuss how the solutions generalize previous works in databases and AI on creating semantic mappings.
590 $a School code: 0250.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 0 $a University of Washington. $3 545923
773 0 $t Dissertation Abstracts International $g 63-11B.
790 1 0 $a Halevy, Alon Y., $e advisor
790 1 0 $a Domingos, Pedro M., $e advisor
790 $a 0250
791 $a Ph.D.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3072077