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.

TAGREAL 通过从大型文本语料库中检索支持信息，自动生成高质量的查询提示，从而探索 PLM 的知识，实现开放知识图谱完成，并实现了最先进的性能表现。

通过预训练语言模型对文本增强开放知识图谱完成进行优化

Text-Augmented Open Knowledge Graph Completion via Pre-Trained Language  Models

Recent work has presented intriguing results examining the knowledge
contained in language models (LM) by having the LM fill in the blanks of
prompts such as "Obama is a _ by profession". These prompts are usually
manually created, and quite possibly sub-optimal; another prompt such as "Obama
worked as a _" may result in more accurately predicting the correct profession.
Because of this, given an inappropriate prompt, we might fail to retrieve facts
that the LM does know, and thus any given prompt only provides a lower bound
estimate of the knowledge contained in an LM. In this paper, we attempt to more
accurately estimate the knowledge contained in LMs by automatically discovering
better prompts to use in this querying process. Specifically, we propose
mining-based and paraphrasing-based methods to automatically generate
high-quality and diverse prompts, as well as ensemble methods to combine
answers from different prompts. Extensive experiments on the LAMA benchmark for
extracting relational knowledge from LMs demonstrate that our methods can
improve accuracy from 31.1% to 39.6%, providing a tighter lower bound on what
LMs know. We have released the code and the resulting LM Prompt And Query
Archive (LPAQA) at this https URL

本文提出使用基于挖掘和释义的自动化方法来生成高质量且多样化的提示语，并使用模型集成的方法来改进语言模型（LM）中的关系知识的查询。通过 LAMA 基准测试表明，该方法将 LM 的准确性从 31.1% 提升至 39.6%，为 LM 的知识提供了更严格的下限估计。