  Nowadays 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 hybrid and modular
methodology for the task. We compare different methods on two benchmarks and
one real-world biomedical publications dataset, showcasing the potential of our
approach and providing insights on graph embeddings when dealing with noisy
Knowledge Graphs.
