Text-Augmented Open Knowledge Graph Completion via Pre-Trained Language
  Models
Pengcheng Jiang Shivam Agarwal Bowen Jin Xuan Wang Jimeng Sun Jiawei Han
The mission of open knowledge graph (KG) completion is to draw new findings
from known facts. Existing works that augment KG completion require either (1)
factual triples to enlarge the graph reasoning space or (2) manually designed
prompts to extract knowledge from a pre-trained language model (PLM),
exhibiting limited performance and requiring expensive efforts from experts. To
this end, we propose TAGREAL that automatically generates quality query prompts
and retrieves support information from large text corpora to probe knowledge
from PLM for KG completion. The results show that TAGREAL achieves
state-of-the-art performance on two benchmark datasets. We find that TAGREAL
has superb performance even with limited training data, outperforming existing
embedding-based, graph-based, and PLM-based methods.