Dragon: Decision Tree Learning for Link Discovery

The provision of links across RDF knowledge bases is regarded as fundamental to ensure that knowledge bases can be used joined to address real-world needs of applications. The growth of knowledge bases both with respect to their number and size demands the development of time-efficient and accurate approaches for the computation of such links. This is generally done with the aid of machine learning approaches, such as e.g. Decision Trees. While Decision Trees are known to be fast, they are generally outperformed in the link discovery task by the state-of-the-art in terms of quality, i.e. F-measure. In this work, we present Dragon, a fast decision-tree-based approach that is both efficient and accurate. Our approach was evaluated by comparing it with state-of-the-art link discovery approaches as well as the common decision-tree-learning approach J48. Our results suggest that our approach achieves state-of-the-art performance with respect to its F-measure while being 18 times faster on average than existing algorithms for link discovery on RDF knowledge bases. Furthermore, we investigate why Dragon significantly outperforms J48 in terms of link accuracy. We provide an open-source implementation of our algorithm in the LIMES framework.