Heuristic-based configuration learning for linked data instance matching

Abstract

Instance matching in linked data has become increasingly important because of the rapid development of linked data. The goal of instance matching is to detect co-referent instances that refer to the same real-world objects. In order to realize such instances, instance matching systems use a configuration, which specifies the matching properties, similarity measures, and other settings of the matching process. For different repositories, the configuration is varied to adapt with the particular characteristics of the input. Therefore, the automation of configuration creation is very important. In this paper, we propose cLink, a supervised instance matching system for linked data. cLink is enhanced by a heuristic algorithm that learns the optimal configuration on the basic of input repositories. We show that cLink can achieve effective performance even when being given only a small amount of training data. Compared to previous configuration learning algorithms, our algorithm significantly improves the results. Compared to the recent supervised systems, cLink is also consistently better on all tested datasets.