Learning linkage rules using genetic programming

An important problem in Linked Data is the discovery of links between entities which identify the same real world object. These links are often generated based on manually written linkage rules which specify the condition which must be fulfilled for two entities in order to be interlinked. In this paper, we present an approach to automatically generate linkage rules from a set of reference links. Our approach is based on genetic programming and has been implemented in the Silk Link Discovery Framework. It is capable of generating complex linkage rules which compare multiple properties of the entities and employ data transformations in order to normalize their values. Experimental results show that it outperforms a genetic programming approach for record deduplication recently presented by Carvalho et. al. In tests with linkage rules that have been created for our research projects our approach learned rules which achieve a similar accuracy than the original human-created linkage rule.