KG-BERT: BERT for Knowledge Graph Completion
This addresses incompleteness in knowledge graphs, which are crucial for AI tasks, by applying a novel method, though it is an incremental adaptation of existing language models.
The authors tackled the problem of incomplete knowledge graphs by proposing KG-BERT, a framework that uses pre-trained language models to treat triples as textual sequences for completion tasks, achieving state-of-the-art performance in triple classification, link prediction, and relation prediction on multiple benchmarks.
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness. In this work, we propose to use pre-trained language models for knowledge graph completion. We treat triples in knowledge graphs as textual sequences and propose a novel framework named Knowledge Graph Bidirectional Encoder Representations from Transformer (KG-BERT) to model these triples. Our method takes entity and relation descriptions of a triple as input and computes scoring function of the triple with the KG-BERT language model. Experimental results on multiple benchmark knowledge graphs show that our method can achieve state-of-the-art performance in triple classification, link prediction and relation prediction tasks.