Uncertainty modeling for inductive knowledge graph embedding

In the process of refining Knowledge Graphs (KGs), new entities emerge, and old entities evolve, which usually updates their attribute information and neighborhood structures. This results in a distribution shift problem for entity features in the embedding space during graph representation learning. Most of existing inductive knowledge graph embedding methods focus mainly on the representation learning of new entities, neglecting the negative impact caused by distribution shift of entity features. In this paper, we use the skill of mean and variance reconstruction to develop a novel inductive knowledge graph embedding model named EDSU for processing the shift of entity feature distribution. Specifically, by assuming that the embedding feature of entity follows multivariate Gaussian distribution, the reconstruction combines the distribution characteristics of components in an entity embedding vector with neighborhood structure information of a set of entity embedding vectors, in order to alleviate the deviation of data information between intra-entity and inter-entity. Furthermore, the connection between the entity features distributions before and after the shift is established, which guides the model training process and provides an interpretation on the rationality of such handling distribution shift in view of distributional data augmentation. Extensive experiments have been conducted and the results demonstrate that our EDSU model outperforms previous state-of-the-art baseline models on inductive link prediction tasks.