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arXiv · 2412.17690

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·Andreas Foltyn·Luzian Hahn·Fabian Kuech
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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.

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Rishiraj Saha Roy, Chris Hinze, Joel Schlotthauer, Farzad Naderi, Viktor Hangya, Andreas Foltyn, Luzian Hahn, Fabian Kuech. 2024-12-23. RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG. https://arxiv.org/abs/2412.17690

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