Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks

Understand

We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings.

• Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available.
• Inspired by the rich semantic correlations between classes at the long tail and those at the head, we take advantage of the knowledge from data-rich classes at the head of the distribution to boost the performance of the data-poor classes at the tail.
• First, we propose to leverage implicit relational knowledge among class labels from knowledge graph embeddings and learn explicit relational knowledge using graph convolution networks.