  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 different LMs. 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.
