  Fine-grained entity typing (FET), which assigns entities in text with
context-sensitive, fine-grained semantic types, is a basic but important task
for knowledge extraction from unstructured text. FET has been studied
extensively in natural language processing and typically relies on
human-annotated corpora for training, which is costly and difficult to scale.
Recent studies explore the utilization of pre-trained language models (PLMs) as
a knowledge base to generate rich and context-aware weak supervision for FET.
However, a PLM still requires direction and guidance to serve as a knowledge
base as they often generate a mixture of rough and fine-grained types, or
tokens unsuitable for typing. In this study, we vision that an ontology
provides a semantics-rich, hierarchical structure, which will help select the
best results generated by multiple PLM models and head words. Specifically, we
propose a novel annotation-free, ontology-guided FET method, OntoType, which
follows a type ontological structure, from coarse to fine, ensembles multiple
PLM prompting results to generate a set of type candidates, and refines its
type resolution, under the local context with a natural language inference
model. Our experiments on the Ontonotes, FIGER, and NYT datasets using their
associated ontological structures demonstrate that our method outperforms the
state-of-the-art zero-shot fine-grained entity typing methods as well as a
typical LLM method, ChatGPT. Our error analysis shows that refinement of the
existing ontology structures will further improve fine-grained entity typing.
