# RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG

arXiv: https://arxiv.org/abs/2412.17690 · HTML: https://mlsanity.com/paper/2412.17690/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [Question Rewriting for Conversational Question Answering](https://mlsanity.com/paper/2004.14652/) (2020)
- [Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph](https://mlsanity.com/paper/1801.10314/) (2018)
- [UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering](https://mlsanity.com/paper/2012.14610/) (2020)
- [SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge Bases](https://mlsanity.com/paper/2003.13956/) (2020)
- [Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion](https://mlsanity.com/paper/1910.03262/) (2019)
- [SPARQL Generation: an analysis on fine-tuning OpenLLaMA for Question Answering over a Life Science Knowledge Graph](https://mlsanity.com/paper/2402.04627/) (2024)
- [Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2105.04850/) (2021)
- [Assessing SPARQL capabilities of Large Language Models](https://mlsanity.com/paper/2409.05925/) (2024)

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

- [Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering](https://mlsanity.com/paper/2605.27164/) (2026)

## Abstract (arXiv)

Conversational question answering (ConvQA) is a convenient means of searching over RDF knowledge graphs (KGs), where a prevalent approach is to translate natural language questions to SPARQL queries. However, SPARQL has certain shortcomings: (i) it is brittle for complex intents and conversational questions, and (ii) it is not suitable for more abstract needs. Instead, we propose a novel two-pronged system where we fuse: (i) SQL-query results over a database automatically derived from the KG, and (ii) text-search results over verbalizations of KG facts. Our pipeline supports iterative retrieval: when the results of any branch are found to be unsatisfactory, the system can automatically opt for further rounds. We put everything together in a retrieval augmented generation (RAG) setup, where an LLM generates a coherent response from accumulated search results. We demonstrate the superiority of our proposed system over several baselines on a knowledge graph of BMW automobiles.