Prompt-Time Ontology-Driven Symbolic Knowledge Capture with Large Language Models

Tolga Çöplü, Arto Bendiken, Andrii Skomorokhov, Eduard Bateiko, Stephen Cobb
5/22/2024
cs.AIcs.CL

Official code: HaltiaAI/paper-PTODSKC (9 stars, last commit 2025-05-19).

Abstract

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

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This repository contains code and access to the dataset used for our paper titled `Prompt-Time Ontology-Driven Symbolic Knowledge Capture with Large Language Models`

92PythonMay 21, 20241 years ago

Cite this paper

@article{pl2024prompttime,
  title  = {Prompt-Time Ontology-Driven Symbolic Knowledge Capture with Large Language Models},
  author = {Tolga Çöplü and Arto Bendiken and Andrii Skomorokhov and Eduard Bateiko and Stephen Cobb},
  year   = {2024},
  eprint = {2405.14012},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.14012v1}
}

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