# Using Multiple RDF Knowledge Graphs for Enriching ChatGPT Responses

arXiv: https://arxiv.org/abs/2304.05774 · HTML: https://mlsanity.com/paper/2304.05774/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [REL: An Entity Linker Standing on the Shoulders of Giants](https://mlsanity.com/paper/2006.01969/) (2020)
- [ChatGPT versus Traditional Question Answering for Knowledge Graphs: Current Status and Future Directions Towards Knowledge Graph Chatbots](https://mlsanity.com/paper/2302.06466/) (2023)

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

- [Harnessing the Power of Semi-Structured Knowledge and LLMs with Triplet-Based Prefiltering for Question Answering](https://mlsanity.com/paper/2409.00861/) (2024)
- [Autofocus Retrieval: An Effective Pipeline for Multi-Hop Question Answering With Semi-Structured Knowledge](https://mlsanity.com/paper/2505.09246/) (2025)
- [Building A Knowledge Graph to Enrich ChatGPT Responses in Manufacturing Service Discovery](https://mlsanity.com/paper/2404.06571/) (2024)
- [Validating ChatGPT Facts through RDF Knowledge Graphs and Sentence Similarity](https://mlsanity.com/paper/2311.04524/) (2023)

## Abstract (arXiv)

There is a recent trend for using the novel Artificial Intelligence ChatGPT chatbox, which provides detailed responses and articulate answers across many domains of knowledge. However, in many cases it returns plausible-sounding but incorrect or inaccurate responses, whereas it does not provide evidence. Therefore, any user has to further search for checking the accuracy of the answer or/and for finding more information about the entities of the response. At the same time there is a high proliferation of RDF Knowledge Graphs (KGs) over any real domain, that offer high quality structured data. For enabling the combination of ChatGPT and RDF KGs, we present a research prototype, called GPToLODS, which is able to enrich any ChatGPT response with more information from hundreds of RDF KGs. In particular, it identifies and annotates each entity of the response with statistics and hyperlinks to LODsyndesis KG (which contains integrated data from 400 RDF KGs and over 412 million entities). In this way, it is feasible to enrich the content of entities and to perform fact checking and validation for the facts of the response at real time.