Service robots need common-sense knowledge to help humans in everyday
situations as it enables them to understand the context of their actions.
However, approaches that use ontologies face a challenge because common-sense
knowledge is often implicit, i.e., it is obvious to humans but not explicitly
stated. This paper investigates if Large Language Models (LLMs) can fill this
gap. Our experiments reveal limited effectiveness in the selective extraction
of contextual action knowledge, suggesting that LLMs may not be sufficient on
their own. However, the large-scale extraction of general, actionable knowledge
shows potential, indicating that LLMs can be a suitable tool for efficiently
creating ontologies for robots. This paper shows that the technique used for
knowledge extraction can be applied to populate a minimalist ontology,
showcasing the potential of LLMs in synergy with formal knowledge
representation.

本研究使用大型语言模型（LLMs）研究了服务机器人需要常识知识帮助人类在日常情境中，以便他们理解其行为的上下文。实验揭示了在选择性提取情境行动知识方面的有限效果，表明单独使用 LLMs 可能不足够。然而，大规模提取一般性、可行性知识显示出潜力，表明 LLMs 可以成为有效创建机器人本体论的工具。该论文展示了知识提取技术可以应用于填充极简本体论，展示了 LLMs 与形式化知识表示的潜在合作能力。