  Most of the existing knowledge graphs are not usually complete and can be
complemented by some reasoning algorithms. The reasoning method based on path
features is widely used in the field of knowledge graph reasoning and
completion on account of that its have strong interpretability. However,
reasoning methods based on path features still have several problems in the
following aspects: Path search isinefficient, insufficient paths for sparse
tasks and some paths are not helpful for reasoning tasks. In order to solve the
above problems, this paper proposes a method called DC-Path that combines
dynamic relation confidence and other indicators to evaluate path features, and
then guide path search, finally conduct relation reasoning. Experimental result
show that compared with the existing relation reasoning algorithm, this method
can select the most representative features in the current reasoning task from
the knowledge graph and achieve better performance on the current relation
reasoning task.
