  Knowledge graph (KG) refinement mainly aims at KG completion and correction
(i.e., error detection). However, most conventional KG embedding models only
focus on KG completion with an unreasonable assumption that all facts in KG
hold without noises, ignoring error detection which also should be significant
and essential for KG refinement.In this paper, we propose a novel
support-confidence-aware KG embedding framework (SCEF), which implements KG
completion and correction simultaneously by learning knowledge representations
with both triple support and triple confidence. Specifically, we build model
energy function by incorporating conventional translation-based model with
support and confidence. To make our triple support-confidence more sufficient
and robust, we not only consider the internal structural information in KG,
studying the approximate relation entailment as triple confidence constraints,
but also the external textual evidence, proposing two kinds of triple supports
with entity types and descriptions respectively.Through extensive experiments
on real-world datasets, we demonstrate SCEF's effectiveness.
