Abstract
Symbolic knowledge graphs (KGs) have been constructed either by expensive human crowdsourcing or with domain-specific complex information extraction pipelines. The emerging large pretrained language models (LMs), such as Bert, have shown to implicitly encode massive knowledge which can be queried with properly designed prompts. However, compared to the explicit KGs, the implict knowledge in the black-box LMs is often difficult to access or edit and lacks explainability. In this work, we aim at harvesting symbolic KGs from the LMs, a new framework for automatic KG construction empowered by the neural LMs' flexibility and scalability. Compared to prior works that often rely on large human annotated data or existing massive KGs, our approach requires only the minimal definition of relations as inputs, and hence is suitable for extracting knowledge of rich new relations not available before.The approach automatically generates diverse prompts, and performs efficient knowledge search within a given LM for consistent and extensive outputs. The harvested knowledge with our approach is substantially more accurate than with previous methods, as shown in both automatic and human evaluation. As a result, we derive from diverse LMs a family of new KGs (e.g., BertNet and RoBERTaNet) that contain a richer set of commonsense relations, including complex ones (e.g., "A is capable of but not good at B"), than the human-annotated KGs (e.g., ConceptNet). Besides, the resulting KGs also serve as a vehicle to interpret the respective source LMs, leading to new insights into the varying knowledge capability of different LMs.
Abstract (translated)
URL
https://arxiv.org/abs/2206.14268
