  Knowledge graph embedding aims at modeling entities and relations with
low-dimensional vectors. Most previous methods require that all entities should
be seen during training, which is unpractical for real-world knowledge graphs
with new entities emerging on a daily basis. Recent efforts on this issue
suggest training a neighborhood aggregator in conjunction with the conventional
entity and relation embeddings, which may help embed new entities inductively
via their existing neighbors. However, their neighborhood aggregators neglect
the unordered and unequal natures of an entity's neighbors. To this end, we
summarize the desired properties that may lead to effective neighborhood
aggregators. We also introduce a novel aggregator, namely, Logic Attention
Network (LAN), which addresses the properties by aggregating neighbors with
both rules- and network-based attention weights. By comparing with conventional
aggregators on two knowledge graph completion tasks, we experimentally validate
LAN's superiority in terms of the desired properties.
