Ensemble approaches for link prediction

Abstract

Knowledge Graphs (KGs) are fundamental for organizing and representing large amounts of information, but they often suffer from incompleteness. Link prediction using Knowledge Graph Embedding (KGE) methods has emerged as a solution to this problem. Many different methods have been proposed to perform link prediction, some of which are a combination of different methods. However, existing approaches that combine different methods typically train models on the entire graph, lacking the diversity seen in machine learning ensembles such as bagging and random forests. In this thesis, we present the novel ensemble approaches UnifEnt and UnifFeat, that divide the KG into sub-graphs by taking advantage of the core principles of bagging and random forests. We evaluated our approach on common KG datasets and showed the benefits of using our method by comparing it to common KGE baseline methods, as well as related work in the area of ensemble methods for link prediction.