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.

翻译：开放知识图谱（KG）补全的任务是从已知事实中挖掘新发现。现有增强KG补全的工作要么需要（1）事实三元组以扩大图推理空间，要么需要（2）人工设计的提示从预训练语言模型（PLM）中提取知识，这些方法表现有限且需要专家大量投入。为此，我们提出TAGREAL，该方法能自动生成高质量查询提示并从大规模文本语料中检索支持信息，以探测PLM中的知识用于KG补全。结果表明，TAGREAL在两个基准数据集上达到了最先进的性能。我们还发现，即便在训练数据有限的情况下，TAGREAL仍表现出卓越性能，超越现有的基于嵌入、基于图和基于PLM的方法。