  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.
