  In the current digitalization era, capturing and effectively representing
knowledge is crucial in most real-world scenarios. In this context, knowledge
graphs represent a potent tool for retrieving and organizing a vast amount of
information in a properly interconnected and interpretable structure. However,
their generation is still challenging and often requires considerable human
effort and domain expertise, hampering the scalability and flexibility across
different application fields. This paper proposes an innovative knowledge graph
generation approach that leverages the potential of the latest generative large
language models, such as GPT-3.5, that can address all the main critical issues
in knowledge graph building. The approach is conveyed in a pipeline that
comprises novel iterative zero-shot and external knowledge-agnostic strategies
in the main stages of the generation process. Our unique manifold approach may
encompass significant benefits to the scientific community. In particular, the
main contribution can be summarized by: (i) an innovative strategy for
iteratively prompting large language models to extract relevant components of
the final graph; (ii) a zero-shot strategy for each prompt, meaning that there
is no need for providing examples for "guiding" the prompt result; (iii) a
scalable solution, as the adoption of LLMs avoids the need for any external
resources or human expertise. To assess the effectiveness of our proposed
model, we performed experiments on a dataset that covered a specific domain. We
claim that our proposal is a suitable solution for scalable and versatile
knowledge graph construction and may be applied to different and novel
contexts.
