# Ontology Population using LLMs

arXiv: https://arxiv.org/abs/2411.01612 · HTML: https://mlsanity.com/paper/2411.01612/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 3 Nov 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [Large Language Models and Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2308.06374/) (2023)
- [Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning](https://mlsanity.com/paper/2304.02711/) (2023)
- [Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text](https://mlsanity.com/paper/2308.02357/) (2023)
- [Towards Ontology Construction with Language Models](https://mlsanity.com/paper/2309.09898/) (2023)
- [MODL: A Modular Ontology Design Library](https://mlsanity.com/paper/1904.05405/) (2019)
- [Ontology Design Facilitating Wikibase Integration -- and a Worked Example for Historical Data](https://mlsanity.com/paper/2205.14032/) (2022)
- [Commonsense Ontology Micropatterns](https://mlsanity.com/paper/2402.18715/) (2024)
- [The KnowWhereGraph Ontology](https://mlsanity.com/paper/2410.13948/) (2024)

## Built on by (most cited first, 4 of 4)

- [Retrieval-Augmented Generation of Ontologies from Relational Databases](https://mlsanity.com/paper/2506.01232/) (2025)
- [Towards Automated Ontology Generation from Unstructured Text: A Multi-Agent LLM Approach](https://mlsanity.com/paper/2604.23090/) (2026)
- [Knowledge Conceptualization Impacts RAG Efficacy](https://mlsanity.com/paper/2507.09389/) (2025)
- [Extracting Semantics: LLM-Guided Automatic Population of Robot Ontology from URDF](https://mlsanity.com/paper/2606.17073/) (2026)

## Abstract (arXiv)

Knowledge graphs (KGs) are increasingly utilized for data integration, representation, and visualization. While KG population is critical, it is often costly, especially when data must be extracted from unstructured text in natural language, which presents challenges, such as ambiguity and complex interpretations. Large Language Models (LLMs) offer promising capabilities for such tasks, excelling in natural language understanding and content generation. However, their tendency to ``hallucinate'' can produce inaccurate outputs. Despite these limitations, LLMs offer rapid and scalable processing of natural language data, and with prompt engineering and fine-tuning, they can approximate human-level performance in extracting and structuring data for KGs. This study investigates LLM effectiveness for the KG population, focusing on the Enslaved.org Hub Ontology. In this paper, we report that compared to the ground truth, LLM's can extract ~90% of triples, when provided a modular ontology as guidance in the prompts.