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

AAAI 2021

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

Feb 17, 2021
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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)

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