@inproceedings{hao-etal-2023-bertnet,
    title = "{B}ert{N}et: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models",
    author = "Hao, Shibo  and
      Tan, Bowen  and
      Tang, Kaiwen  and
      Ni, Bin  and
      Shao, Xiyan  and
      Zhang, Hengzhe  and
      Xing, Eric  and
      Hu, Zhiting",
    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.309",
    doi = "10.18653/v1/2023.findings-acl.309",
    pages = "5000--5015",
    abstract = "It is crucial to automatically construct knowledge graphs (KGs) of diverse new relations to support knowledge discovery and broad applications. Previous KG construction methods, based on either crowdsourcing or text mining, are often limited to a small predefined set of relations due to manual cost or restrictions in text corpus. Recent research proposed to use pretrained language models (LMs) as implicit knowledge bases that accept knowledge queries with prompts. Yet, the implicit knowledge lacks many desirable properties of a full-scale symbolic KG, such as easy access, navigation, editing, and quality assurance. In this paper, we propose a new approach of harvesting massive KGs of arbitrary relations from pretrained LMs. With minimal input of a relation definition (a prompt and a few shot of example entity pairs), the approach efficiently searches in the vast entity pair space to extract diverse accurate knowledge of the desired relation. We develop an effective search-and-rescore mechanism for improved efficiency and accuracy. We deploy the approach to harvest KGs of over 400 new relations, from LMs of varying capacities such as RoBERTaNet. Extensive human and automatic evaluations show our approach manages to extract diverse accurate knowledge, including tuples of complex relations (e.g., {``}A is capable of but not good at B{''}). The resulting KGs as a symbolic interpretation of the source LMs also reveal new insights into the LMs{'} knowledge capacities.",
}

It is crucial to automatically construct knowledge graphs (KGs) of diverse new relations to support knowledge discovery and broad applications. Previous KG construction methods, based on either crowdsourcing or text mining, are often limited to a small predefined set of relations due to manual cost or restrictions in text corpus. Recent research proposed to use pretrained language models (LMs) as implicit knowledge bases that accept knowledge queries with prompts. Yet, the implicit knowledge lacks many desirable properties of a full-scale symbolic KG, such as easy access, navigation, editing, and quality assurance. In this paper, we propose a new approach of harvesting massive KGs of arbitrary relations from pretrained LMs. With minimal input of a relation definition (a prompt and a few shot of example entity pairs), the approach efficiently searches in the vast entity pair space to extract diverse accurate knowledge of the desired relation. We develop an effective search-and-rescore mechanism for improved efficiency and accuracy. We deploy the approach to harvest KGs of over 400 new relations, from LMs of varying capacities such as RoBERTaNet. Extensive human and automatic evaluations show our approach manages to extract diverse accurate knowledge, including tuples of complex relations (e.g., {``}A is capable of but not good at B{''}). The resulting KGs as a symbolic interpretation of the source LMs also reveal new insights into the LMs{'} knowledge capacities.
Hao, Shibo Tan, Bowen Tang, Kaiwen Ni, Bin Shao, Xiyan Zhang, Hengzhe Xing, Eric Hu, Zhiting
BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models