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

Rishiraj Saha Roy, Chris Hinze, Joel Schlotthauer, Farzad Naderi, Viktor Hangya +3 more
12/23/2024
cs.CLcs.IR

No confident code implementation found for this paper.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering

84% match1 repo52025

WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphs

84% match2024

RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking

83% match1 repo322025

DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation

83% match2025

In-depth Analysis of Graph-based RAG in a Unified Framework

83% match2025

Knowledge Graph-extended Retrieval Augmented Generation for Question Answering

83% match2025

Cite this paper

@article{roy2024ragonite,
  title  = {RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG},
  author = {Rishiraj Saha Roy and Chris Hinze and Joel Schlotthauer and Farzad Naderi and Viktor Hangya and Andreas Foltyn and Luzian Hahn and Fabian Kuech},
  year   = {2024},
  eprint = {2412.17690},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.17690v3}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2412.17690.svg)](https://codeofpaper.com/paper/2412.17690)

Discussion