A comparison of supervised learning classifiers for link discovery

Abstract

The detection of links between resources is intrinsic to the vision of the Linked Data Web. Due to the mere size of current knowledge bases, this task is commonly addressed by using tools. In particular, manifold link discovery frameworks have been developed. These frameworks implement several different machine-learning approaches to discovering links. In this paper, we investigate which of the commonly used supervised machine-learning classifiers performs best on the link discovery task. To this end, we first present our evaluation pipeline. Then, we compare ten different approaches on three artificial and three real-world benchmark data sets. The classification outcomes are subsequently compared with several state-of-the-art frameworks. Our results suggest that while several algorithms perform well, multilayer perceptrons perform best on average. Moreover, logistic regression seems best suited for noisy data.