  In applications such as personal assistants, large language models (LLMs)
must consider the user's personal information and preferences. However, LLMs
lack the inherent ability to learn from user interactions. This paper explores
capturing personal information from user prompts using ontology and
knowledge-graph approaches. We use a subset of the KNOW ontology, which models
personal information, to train the language model on these concepts. We then
evaluate the success of knowledge capture using a specially constructed
dataset. Our code and datasets are publicly available at
https://github.com/HaltiaAI/paper-PTODSKC
