mlsanityData to 14 Oct 2026

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

arXiv 2412.17690 ↗First posted 23 Dec 2024cs.CL cs.IRcited by 1

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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.

Key facts. Cited by 1 paper 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

12 of 21 references.

References · 12

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  3. 2024Mixtral of Expertscited by 1,823
  4. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 688
  5. 2020Question Rewriting for Conversational Question Answeringcited by 93
  6. 2018Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graphcited by 67
  7. 2020UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answeringcited by 61
  8. 2020SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge Basescited by 47
  9. 2019Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansioncited by 42
  10. 2024SPARQL Generation: an analysis on fine-tuning OpenLLaMA for Question Answering over a Life Science Knowledge Graphcited by 19
  11. 2021Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphscited by 18
  12. 2024Assessing SPARQL capabilities of Large Language Modelscited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answeringcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering
  2. 2025Clue-RAG: Towards Accurate and Cost-Efficient Graph-based RAG via Multi-Partite Graph and Query-Driven Iterative Retrieval
  3. 2025Beyond Static Retrieval: Opportunities and Pitfalls of Iterative Retrieval in GraphRAG
  4. 2026Augmenting Question Answering with A Hybrid RAG Approach
  5. 2026RPO-RAG: Aligning Small LLMs with Relation-aware Preference Optimization for Knowledge Graph Question Answering
  6. 2024Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check
  7. 2025FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphs
  8. 2025DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation