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Learning object identification rules...
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Tejada, Sheila Ann.
Learning object identification rules for information integration.
紀錄類型:
書目-電子資源 : 單行本
正題名/作者:
Learning object identification rules for information integration./
作者:
Tejada, Sheila Ann.
面頁冊數:
108 p.
附註:
Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4469.
Contained By:
Dissertation Abstracts International64-09B.
標題:
Computer Science. -
電子資源:
Download fulltext (下載全文)
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:
1000005419
Computer Science.
Learning object identification rules for information integration.
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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.
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