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

arXiv: https://arxiv.org/abs/2405.14012 · HTML: https://mlsanity.com/paper/2405.14012/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 22 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 16 of 16)

- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Entity-Relation Extraction as Multi-Turn Question Answering](https://mlsanity.com/paper/1905.05529/) (2019)
- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text](https://mlsanity.com/paper/2308.02357/) (2023)
- [LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT](https://mlsanity.com/paper/2307.06917/) (2023)
- [CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training](https://mlsanity.com/paper/2006.04702/) (2020)
- [Towards Ontology Construction with Language Models](https://mlsanity.com/paper/2309.09898/) (2023)
- [Neurosymbolic AI for Reasoning over Knowledge Graphs: A Survey](https://mlsanity.com/paper/2302.07200/) (2023)
- [Neurosymbolic AI -- Why, What, and How](https://mlsanity.com/paper/2305.00813/) (2023)
- [Ontology engineering with Large Language Models](https://mlsanity.com/paper/2307.16699/) (2023)
- [PIVOINE: Instruction Tuning for Open-world Information Extraction](https://mlsanity.com/paper/2305.14898/) (2023)
- [Ontology Guided Information Extraction from Unstructured Text](https://mlsanity.com/paper/1302.1335/) (2013)
- [Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG](https://mlsanity.com/paper/2303.15113/) (2023)
- [INFINITY: A Simple Yet Effective Unsupervised Framework for Graph-Text Mutual Conversion](https://mlsanity.com/paper/2209.10754/) (2022)
- [Prompt-Time Symbolic Knowledge Capture with Large Language Models](https://mlsanity.com/paper/2402.00414/) (2024)

## Built on by (most cited first, 2 of 2)

- [Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches](https://mlsanity.com/paper/2501.03151/) (2025)
- [KNOW: A Real-World Ontology for Knowledge Capture with Large Language Models](https://mlsanity.com/paper/2405.19877/) (2024)

## Abstract (arXiv)

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