InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions

Understand

Most existing knowledge graphs suffer from incompleteness, which can be alleviated by inferring missing links based on known facts.

• One popular way to accomplish this is to generate low-dimensional embeddings of entities and relations, and use these to make inferences.
• ConvE, a recently proposed approach, applies convolutional filters on 2D reshapings of entity and relation embeddings in order to capture rich interactions between their components.
• However, the number of interactions that ConvE can capture is limited.