  Knowledge graphs (KGs) are widely used to facilitate relation extraction (RE)
tasks. While most previous RE methods focus on leveraging deterministic KGs,
uncertain KGs, which assign a confidence score for each relation instance, can
provide prior probability distributions of relational facts as valuable
external knowledge for RE models. This paper proposes to exploit uncertain
knowledge to improve relation extraction. Specifically, we introduce ProBase,
an uncertain KG that indicates to what extent a target entity belongs to a
concept, into our RE architecture. We then design a novel multi-view inference
framework to systematically integrate local context and global knowledge across
three views: mention-, entity- and concept-view. The experimental results show
that our model achieves competitive performances on both sentence- and
document-level relation extraction, which verifies the effectiveness of
introducing uncertain knowledge and the multi-view inference framework that we
design.
