  Multi-hop reasoning has been widely studied in recent years to seek an
effective and interpretable method for knowledge graph (KG) completion. Most
previous reasoning methods are designed for dense KGs with enough paths between
entities, but cannot work well on those sparse KGs that only contain sparse
paths for reasoning. On the one hand, sparse KGs contain less information,
which makes it difficult for the model to choose correct paths. On the other
hand, the lack of evidential paths to target entities also makes the reasoning
process difficult. To solve these problems, we propose a multi-hop reasoning
model named DacKGR over sparse KGs, by applying novel dynamic anticipation and
completion strategies: (1) The anticipation strategy utilizes the latent
prediction of embedding-based models to make our model perform more potential
path search over sparse KGs. (2) Based on the anticipation information, the
completion strategy dynamically adds edges as additional actions during the
path search, which further alleviates the sparseness problem of KGs. The
experimental results on five datasets sampled from Freebase, NELL and Wikidata
show that our method outperforms state-of-the-art baselines. Our codes and
datasets can be obtained from https://github.com/THU-KEG/DacKGR
