Paper Info
Title
Active learning of expressive linkage rules for the web of data
Abstract
The amount of data that is available as Linked Data on the Web has grown rapidly over the last years. However, the linkage between data sources remains sparse as setting RDF links means effort for the data publishers. Many existing methods for generating these links rely on explicit linkage rules which specify the conditions which must hold true for two entities in order to be interlinked. As writing good linkage rules by hand is a non-trivial problem, the burden to generate links between data sources is still high. In order to reduce the effort and required expertise to write linkage rules, we present an approach which combines genetic programming and active learning for the interactive generation of expressive linkage rules. Our approach automates the generation of a linkage rule and only requires the user to confirm or decline a number of example links. The algorithm minimizes user involvement by selecting example links which yield a high information gain. The proposed approach has been implemented in the Silk Link Discovery Framework. Within our experiments, the algorithm was capable of finding linkage rules with a full F1-measure by asking the user to confirm or decline a maximum amount of 20 links.
Year
DOI
Venue
2012
10.1007/978-3-642-31753-8_34
ICWE
Keywords
Field
DocType
data publisher,expressive linkage rule,linkage rule,active learning,algorithm minimizes user involvement,example link,good linkage rule,high information gain,explicit linkage rule,data source
Data mining,World Wide Web,Active learning,Computer science,Information gain,Linked data,Genetic programming,Artificial intelligence,Machine learning,RDF
Conference
Citations 
PageRank 
References 
10
0.60
7
Authors
3
Name
Order
Citations
PageRank
Robert Isele148925.24
Anja Jentzsch250926.80
Christian Bizer38448524.93