Paper Info
Title
Learning expressive linkage rules using genetic programming
Abstract
A central problem in data integration and data cleansing is to find entities in different data sources that describe the same real-world object. Many existing methods for identifying such entities rely on explicit linkage rules which specify the conditions that entities must fulfill in order to be considered to describe the same real-world object. In this paper, we present the GenLink algorithm for learning expressive linkage rules from a set of existing reference links using genetic programming. The algorithm is capable of generating linkage rules which select discriminative properties for comparison, apply chains of data transformations to normalize property values, choose appropriate distance measures and thresholds and combine the results of multiple comparisons using non-linear aggregation functions. Our experiments show that the GenLink algorithm outperforms the state-of-the-art genetic programming approach to learning linkage rules recently presented by Carvalho et. al. and is capable of learning linkage rules which achieve a similar accuracy as human written rules for the same problem.
Year
DOI
Venue
2012
10.14778/2350229.2350276
PVLDB
Keywords
DocType
Volume
central problem,expressive linkage rule,linkage rule,different data source,data integration,data cleansing,real-world object,genlink algorithm,genetic programming,data transformation,explicit linkage rule
Journal
5
Issue
ISSN
Citations 
11
Proceedings of the VLDB Endowment (PVLDB), Vol. 5, No. 11, pp. 1638-1649 (2012)
44
PageRank 
References 
Authors
1.23
18
2
Name
Order
Citations
PageRank
Robert Isele148925.24
Christian Bizer28448524.93