Inductive Link Prediction in Knowledge Graphs using Path-based Neural
  Networks
Canlin Zhang Xiuwen Liu
Link prediction is a crucial research area in knowledge graphs, with many
downstream applications. In many real-world scenarios, inductive link
prediction is required, where predictions have to be made among unseen
entities. Embedding-based models usually need fine-tuning on new entity
embeddings, and hence are difficult to be directly applied to inductive link
prediction tasks. Logical rules captured by rule-based models can be directly
applied to new entities with the same graph typologies, but the captured rules
are discrete and usually lack generosity. Graph neural networks (GNNs) can
generalize topological information to new graphs taking advantage of deep
neural networks, which however may still need fine-tuning on new entity
embeddings. In this paper, we propose SiaILP, a path-based model for inductive
link prediction using siamese neural networks. Our model only depends on
relation and path embeddings, which can be generalized to new entities without
fine-tuning. Experiments show that our model achieves several new
state-of-the-art performances in link prediction tasks using inductive versions
of WN18RR, FB15k-237, and Nell995.