@inproceedings{jiang-etal-2023-text,
    title = "Text Augmented Open Knowledge Graph Completion via Pre-Trained Language Models",
    author = "Jiang, Pengcheng  and
      Agarwal, Shivam  and
      Jin, Bowen  and
      Wang, Xuan  and
      Sun, Jimeng  and
      Han, Jiawei",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.709",
    doi = "10.18653/v1/2023.findings-acl.709",
    pages = "11161--11180",
    abstract = "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.",
}

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.
Jiang, Pengcheng Agarwal, Shivam Jin, Bowen Wang, Xuan Sun, Jimeng Han, Jiawei
Text Augmented Open Knowledge Graph Completion via Pre-Trained Language Models