# Ontology Learning and Knowledge Graph Construction: A Comparison of Approaches and Their Impact on RAG Performance

arXiv: https://arxiv.org/abs/2511.05991 · HTML: https://mlsanity.com/paper/2511.05991/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 8 Nov 2025.

## Placed in

- Retrieval-augmented generation / [Knowledge-graph and structured-knowledge RAG](https://mlsanity.com/rag/knowledge-graph-and-structured-knowledge-rag/)

## Builds on (most cited first, 2 of 2)

- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [DINGO: an ontology for projects and grants linked data](https://mlsanity.com/paper/2006.13438/) (2020)

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

- [Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents](https://mlsanity.com/paper/2604.00555/) (2026)
- [Graphs Don't Stay Secret: Practical Subgraph Reconstruction Attacks on Defended Graph RAG](https://mlsanity.com/paper/2602.06495/) (2026)
- [Transforming OPACs into Intelligent Discovery Systems: An AI-Powered, Knowledge Graph-Driven Smart OPAC for Digital Libraries](https://mlsanity.com/paper/2604.01262/) (2026)
- [When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning](https://mlsanity.com/paper/2608.31118/) (2026)

## Abstract (arXiv)

Retrieval-Augmented Generation (RAG) systems combine Large Language Models (LLMs) with external knowledge, and their performance depends heavily on how that knowledge is represented. This study investigates how different Knowledge Graph (KG) construction strategies influence RAG performance. We compare a variety of approaches: standard vector-based RAG, GraphRAG, and retrieval over KGs built from ontologies derived either from relational databases or textual corpora. Results show that ontology-guided KGs incorporating chunk information achieve competitive performance with state-of-the-art frameworks, substantially outperforming vector retrieval baselines. Moreover, the findings reveal that ontology-guided KGs built from relational databases perform competitively to ones built with ontologies extracted from text, with the benefit of offering a dual advantage: they require a one-time-only ontology learning process, substantially reducing LLM usage costs; and avoid the complexity of ontology merging inherent to text-based approaches.