Error detection in Knowledge Graphs: Path Ranking,Embeddings or both?

This paper attempts to compare and combine different approaches for detecting errors in Knowledge Graphs. Knowledge Graphs constitute a mainstream approach for the representation of relational information on big heterogeneous data, however, they may contain a big amount of imputed noise when constructed automatically. To address this problem, different error detection methodologies have been proposed, mainly focusing on path ranking and representation learning. This work presents various mainstream approaches and proposes a novel hybrid and modular methodology for the task. We compare these methods on two benchmarks and one real-world biomedical publications dataset, showcasing the potential of our approach and drawing insights regarding the state-of-art in error detection in Knowledge Graphs