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Title: Text Augmented Open Knowledge Graph Completion via Pre-Trained Language Models
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.  more » « less
Award ID(s):
2118329
PAR ID:
10543122
Author(s) / Creator(s):
; ; ; ; ;
Editor(s):
Rogers, Anna; Boyd-Graber, Jordan; Okazaki, Naoaki
Publisher / Repository:
Association for Computational Linguistics
Date Published:
Page Range / eLocation ID:
11161 to 11180
Format(s):
Medium: X
Location:
Toronto, Canada
Sponsoring Org:
National Science Foundation
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