KBGAN: Adversarial Learning for Knowledge Graph Embeddings

Understand

We introduce KBGAN, an adversarial learning framework to improve the performances of a wide range of existing knowledge graph embedding models.

• Because knowledge graphs typically only contain positive facts, sampling useful negative training examples is a non-trivial task.
• Replacing the head or tail entity of a fact with a uniformly randomly selected entity is a conventional method for generating negative facts, but the majority of the generated negative facts can be easily discriminated from positive facts, and will contribute little towards the training.
• Inspired by generative adversarial networks (GANs), we use one knowledge graph embedding model as a negative sample generator to assist the training of our desired model, which acts as the discriminator in GANs.