  Taxonomies represent an arborescence hierarchical structure that establishes
relationships among entities to convey knowledge within a specific domain. Each
edge in the taxonomy signifies a hypernym-hyponym relationship. Taxonomies find
utility in various real-world applications, such as e-commerce search engines
and recommendation systems. Consequently, there arises a necessity to enhance
these taxonomies over time. However, manually curating taxonomies with neoteric
data presents challenges due to limitations in available human resources and
the exponential growth of data. Therefore, it becomes imperative to develop
automatic taxonomy expansion methods. Traditional supervised taxonomy expansion
approaches encounter difficulties stemming from limited resources, primarily
due to the small size of existing taxonomies. This scarcity of training data
often leads to overfitting. In this paper, we propose FLAME, a novel approach
for taxonomy expansion in low-resource environments by harnessing the
capabilities of large language models that are trained on extensive real-world
knowledge. LLMs help compensate for the scarcity of domain-specific knowledge.
Specifically, FLAME leverages prompting in few-shot settings to extract the
inherent knowledge within the LLMs, ascertaining the hypernym entities within
the taxonomy. Furthermore, it employs reinforcement learning to fine-tune the
large language models, resulting in more accurate predictions. Experiments on
three real-world benchmark datasets demonstrate the effectiveness of FLAME in
real-world scenarios, achieving a remarkable improvement of 18.5% in accuracy
and 12.3% in Wu & Palmer metric over eight baselines. Furthermore, we elucidate
the strengths and weaknesses of FLAME through an extensive case study, error
analysis and ablation studies on the benchmarks.
