Abstract

The task of object identification occurs 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. Previous methods of object identification have required manual construction of domain-specific string transformations or manual setting of general transformation parameter weights for recognizing format inconsistencies. This manual process can be time consuming and error-prone. We have developed an object identification system called Active Atlas [18], which applies a set of domain-independent string transformations to compare the objects' shared attributes in order to identify matching objects. In this paper, we discuss extensions to the Active Atlas system, which allow it to learn to tailor the weights of a set of general transformations to a specific application domain through limited …

Date

July 23, 2002

Authors

Sheila Tejada, Craig A Knoblock, Steven Minton

Book

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages

350-359