Abstract: This paper attempts to compare and combine different approaches for de-tecting errors in Knowledge Graphs. Knowledge Graphs constitute a mainstreamapproach for the representation of relational information on big heterogeneous data,however, they may contain a big amount of imputed noise when constructed auto-matically. To address this problem, different error detection methodologies have beenproposed, mainly focusing on path ranking and representation learning. This workpresents various mainstream approaches and proposes a novel hybrid and modularmethodology for the task. We compare these methods on two benchmarks and one real-world biomedical publications dataset, showcasing the potential of our approach anddrawing insights regarding the state-of-art in error detection in Knowledge Graphs.
Authors: Konstantinos Bougiatiotis, Romanos Fasoulis, Fotis Aisopos, Anastasios Nentidis, Georgios Paliouras (National Center Scientific Research Demokritos, National and Kapodistrian University of Athens)