  Knowledge Graph (KG) errors introduce non-negligible noise, severely
affecting KG-related downstream tasks. Detecting errors in KGs is challenging
since the patterns of errors are unknown and diverse, while ground-truth labels
are rare or even unavailable. A traditional solution is to construct logical
rules to verify triples, but it is not generalizable since different KGs have
distinct rules with domain knowledge involved. Recent studies focus on
designing tailored detectors or ranking triples based on KG embedding loss.
However, they all rely on negative samples for training, which are generated by
randomly replacing the head or tail entity of existing triples. Such a negative
sampling strategy is not enough for prototyping practical KG errors, e.g.,
(Bruce_Lee, place_of_birth, China), in which the three elements are often
relevant, although mismatched. We desire a more effective unsupervised learning
mechanism tailored for KG error detection. To this end, we propose a novel
framework - ContrAstive knowledge Graph Error Detection (CAGED). It introduces
contrastive learning into KG learning and provides a novel way of modeling KG.
Instead of following the traditional setting, i.e., considering entities as
nodes and relations as semantic edges, CAGED augments a KG into different
hyper-views, by regarding each relational triple as a node. After joint
training with KG embedding and contrastive learning loss, CAGED assesses the
trustworthiness of each triple based on two learning signals, i.e., the
consistency of triple representations across multi-views and the
self-consistency within the triple. Extensive experiments on three real-world
KGs show that CAGED outperforms state-of-the-art methods in KG error detection.
Our codes and datasets are available at https://github.com/Qing145/CAGED.git.

Machine Learning;Artificial Intelligence