The growing trend of Large Language Models (LLM) development has attracted significant attention, with models for various applications emerging consistently. However, the combined application of Large Language Models with semantic technologies for reasoning and inference is still a challenging task. This paper analyzes how the current advances in foundational LLM, like ChatGPT, can be compared with the specialized pretrained models, like REBEL, for joint entity and relation extraction. To evaluate this approach, we conducted several experiments using sustainability-related text as our use case. We created pipelines for the automatic creation of Knowledge Graphs from raw texts, and our findings indicate that using advanced LLM models can improve the accuracy of the process of creating these graphs from unstructured text. Furthermore, we explored the potential of automatic ontology creation using foundation LLM models, which resulted in even more relevant and accurate knowledge graphs.

翻译：大语言模型（LLM）发展的日益增长趋势吸引了广泛关注，各类应用模型不断涌现。然而，将大语言模型与语义技术相结合用于推理与推断仍具挑战性。本文分析了当前基础LLM（如ChatGPT）的进展如何与专用预训练模型（如REBEL）在联合实体关系抽取任务中进行比较。为评估该方法，我们以可持续性相关文本为用例开展系列实验，构建了从原始文本自动生成知识图谱的流水线。研究结果表明，使用先进LLM模型可提升从非结构化文本创建这些图谱的准确性。此外，我们探索了利用基础LLM模型实现自动本体创建的潜力，这一方法最终生成了更具相关性和准确性的知识图谱。