# Development of Ontological Knowledge Bases by Leveraging Large Language Models

arXiv: https://arxiv.org/abs/2601.10436 · HTML: https://mlsanity.com/paper/2601.10436/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 71 references that are in the map.
- First posted on arXiv 15 Jan 2026.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Capabilities of Gemini Models in Medicine](https://mlsanity.com/paper/2404.18416/) (2024)
- [Granite Code Models: A Family of Open Foundation Models for Code Intelligence](https://mlsanity.com/paper/2405.04324/) (2024)
- [Enhancing Knowledge Graph Construction Using Large Language Models](https://mlsanity.com/paper/2305.04676/) (2023)
- [LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT](https://mlsanity.com/paper/2307.06917/) (2023)
- [Towards Human-Bot Collaborative Software Architecting with ChatGPT](https://mlsanity.com/paper/2302.14600/) (2023)
- [How Well Do LLMs Predict Prerequisite Skills? Zero-Shot Comparison to Expert-Defined Concepts](https://mlsanity.com/paper/2507.18479/) (2025)
- [An Automatic Ontology Generation Framework with An Organizational Perspective](https://mlsanity.com/paper/2201.05910/) (2022)
- [Large language models as oracles for instantiating ontologies with domain-specific knowledge](https://mlsanity.com/paper/2404.04108/) (2024)
- [Improving Semantic Similarity Measure Within a Recommender System Based-on RDF Graphs](https://mlsanity.com/paper/2307.10639/) (2023)
- [A Constraint-based Recommender System via RDF Knowledge Graphs](https://mlsanity.com/paper/2307.10702/) (2023)
- [On the Use of Large Language Models to Generate Capability Ontologies](https://mlsanity.com/paper/2404.17524/) (2024)
- [Automated Skill Decomposition Meets Expert Ontologies: Bridging the Granularity Gap with LLMs](https://mlsanity.com/paper/2510.11313/) (2025)
- [Constraint-based recommender system for crisis management simulations](https://mlsanity.com/paper/2306.04553/) (2023)
- [Designing a User Contextual Profile Ontology: A Focus on the Vehicle Sales Domain](https://mlsanity.com/paper/2308.06018/) (2023)

## Built on by (most cited first, 1 of 1)

- [Unifying Ontology Construction and Semantic Alignment for Deterministic Enterprise Reasoning at Scale](https://mlsanity.com/paper/2604.09608/) (2026)

## Abstract (arXiv)

Ontological Knowledge Bases (OKBs) play a vital role in structuring domain-specific knowledge and serve as a foundation for effective knowledge management systems. However, their traditional manual development poses significant challenges related to scalability, consistency, and adaptability. Recent advancements in Generative AI, particularly Large Language Models (LLMs), offer promising solutions for automating and enhancing OKB development. This paper introduces a structured, iterative methodology leveraging LLMs to optimize knowledge acquisition, automate ontology artifact generation, and enable continuous refinement cycles. We demonstrate this approach through a detailed case study focused on developing a user context profile ontology within the vehicle sales domain. Key contributions include significantly accelerated ontology construction processes, improved ontological consistency, effective bias mitigation, and enhanced transparency in the ontology engineering process. Our findings highlight the transformative potential of integrating LLMs into ontology development, notably improving scalability, integration capabilities, and overall efficiency in knowledge management systems.