  Embedding entities and relations into continuous vector spaces has attracted
a surge of interest in recent years. Most embedding methods assume that all
test entities are available during training, which makes it time-consuming to
retrain embeddings for newly emerging entities. To address this issue, recent
works apply the graph neural network on the existing neighbors of the unseen
entities. In this paper, we propose a novel framework, namely Virtual Neighbor
(VN) network, to address three key challenges. Firstly, to reduce the neighbor
sparsity problem, we introduce the concept of the virtual neighbors inferred by
rules. And we assign soft labels to these neighbors by solving a
rule-constrained problem, rather than simply regarding them as unquestionably
true. Secondly, many existing methods only use one-hop or two-hop neighbors for
aggregation and ignore the distant information that may be helpful. Instead, we
identify both logic and symmetric path rules to capture complex patterns.
Finally, instead of one-time injection of rules, we employ an iterative
learning scheme between the embedding method and virtual neighbor prediction to
capture the interactions within. Experimental results on two knowledge graph
completion tasks demonstrate that our VN network significantly outperforms
state-of-the-art baselines. Furthermore, results on Subject/Object-R show that
our proposed VN network is highly robust to the neighbor sparsity problem.
