By Topic

Learning-Based Approaches for Matching Web Data Entities

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Köpcke, H. ; Univ. of Leipzig, Leipzig, Germany ; Thor, A. ; Rahm, E.

Entity matching is a key task for data integration and especially challenging for Web data. Effective entity matching typically requires combining several match techniques and finding suitable configuration parameters, such as similarity thresholds. The authors investigate to what degree machine learning helps semi-automatically determine suitable match strategies with a limited amount of manual training effort. They use a new framework, Fever, to evaluate several learning-based approaches for matching different sets of Web data entities. In particular, they study different approaches for training-data selection and how much training is needed to find effective combined match strategies and configurations.

Published in:

Internet Computing, IEEE  (Volume:14 ,  Issue: 4 )