Unsupervised Link Discovery through Knowledge Base Repair

The Linked Data Web has developed into a compendium of partly very large datasets. Devising efficient approaches to compute links between these datasets is thus central to achieve the vision behind the Data Web. Unsupervised approaches to achieve this goal have emerged over the last few years. Yet, so far, none of these unsupervised approaches makes use of the replication of resources across several knowledge bases to improve the accuracy it achieves while linking. In this paper, we present Colibri, an iterative unsupervised approach for link discovery. Colibri allows the discovery of links between n datasets (n ≥ 2) while improving the quality of the instance data in these datasets. To this end, Colibri combines error detection and correction with unsupervised link discovery. We evaluate our approach on five benchmark datasets with respect to the F-score it achieves. Our results suggest that Colibri can significantly improve the results of unsupervised machine-learning approaches for link discovery while correctly detecting erroneous resources.