Inductive Link Prediction in Knowledge Graphs using Path-based Neural Networks
This addresses the problem of making link predictions for new entities without fine-tuning, which is crucial for real-world applications where knowledge graphs evolve, representing an incremental improvement over existing methods.
The paper tackles inductive link prediction in knowledge graphs, where predictions must be made for unseen entities, and proposes SiaILP, a path-based model using siamese neural networks that achieves new state-of-the-art performances on inductive versions of WN18RR, FB15k-237, and Nell995 datasets.
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. Our code is available at \url{https://github.com/canlinzhang/SiaILP}.