Inductive Knowledge Graph Embedding via Exploring Interaction Patterns of Relations
Chong Mu, Lizong Zhang, Jinchuan Zhang, Qian Huang, Zhiguo Wang · 2024
Recent research in inductive reasoning has focused on predicting missing links between entities that are not observed during training. However, most approaches usually require that the relations are known at the inference time. In the real world, new entities and new relations usually emerge concurrently, which greatly challenges the model's generalization ability. In this paper, we propose a novel inductive knowledge graph embedding model that effectively handles unknown entities and relations by capturing their local structural features. Specifically, a relation graph is constructed to learn relation representations. In the relation graph, we employ a four-dimensional vector to represent the interaction patterns between nodes (relations), where each dimension corresponds to a specific type of interaction. For entity representations, our model dynamically initializes entity features using relation features and attentively aggregates neighboring features of entities to update entity features. By modeling interaction patterns between relations and incorporating structural information of entities, our model learns how to aggregate neighboring embeddings using attention mechanisms, thus generating high-quality embeddings for new entities and relations. Extensive experiments on benchmark datasets demonstrate that our model outperforms state-of-the-art methods, particularly in scenarios involving completely new relations.