  Entity alignment which aims at linking entities with the same meaning from
different knowledge graphs (KGs) is a vital step for knowledge fusion. Existing
research focused on learning embeddings of entities by utilizing structural
information of KGs for entity alignment. These methods can aggregate
information from neighboring nodes but may also bring noise from neighbors.
Most recently, several researchers attempted to compare neighboring nodes in
pairs to enhance the entity alignment. However, they ignored the relations
between entities which are also important for neighborhood matching. In
addition, existing methods paid less attention to the positive interactions
between the entity alignment and the relation alignment. To deal with these
issues, we propose a novel Relation-aware Neighborhood Matching model named RNM
for entity alignment. Specifically, we propose to utilize the neighborhood
matching to enhance the entity alignment. Besides comparing neighbor nodes when
matching neighborhood, we also try to explore useful information from the
connected relations. Moreover, an iterative framework is designed to leverage
the positive interactions between the entity alignment and the relation
alignment in a semi-supervised manner. Experimental results on three real-world
datasets demonstrate that the proposed model RNM performs better than
state-of-the-art methods.
