  Most existing knowledge graphs suffer from incompleteness. Embedding
knowledge graphs into continuous vector spaces has recently attracted
increasing interest in knowledge base completion. However, in most existing
embedding methods, only fact triplets are utilized, and logical rules have not
been thoroughly studied for the knowledge base completion task. To overcome the
problem, we propose an association rules enhanced knowledge graph attention
network (AR-KGAT). The AR-KGAT captures both entity and relation features for
high-order neighborhoods of any given entity in an end-to-end manner under the
graph attention network framework. The major component of AR-KGAT is an encoder
of an effective neighborhood aggregator, which addresses the problems by
aggregating neighbors with both association-rules-based and graph-based
attention weights. Additionally, the proposed model also encapsulates the
representations from multi-hop neighbors of nodes to refine their embeddings.
The decoder enables AR-KGAT to be translational between entities and relations
while keeping the superior link prediction performance. A logic-like inference
pattern is utilized as constraints for knowledge graph embedding. Then, the
global loss is minimized over both atomic and complex formulas to achieve the
embedding task. In this manner, we learn embeddings compatible with triplets
and rules, which are certainly more predictive for knowledge acquisition and
inference. We conduct extensive experiments on two benchmark datasets: WN18RR
and FB15k-237, for two knowledge graph completion tasks: the link prediction
and triplet classification to evaluate the proposed AR-KGAT model. The results
show that the proposed AR-KGAT model achieves significant and consistent
improvements over state-of-the-art methods.
