RAGONITE：基于诱导数据库与RDF语言化的迭代检索用于知识图谱对话式问答的RAG系统
RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG
- Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
RAGONITE提出一种双通道迭代检索框架，融合SQL查询与文本搜索，并结合RAG由LLM生成回答，在BMW汽车知识图谱上优于多个基线。
AI中文摘要：
对话式问答（ConvQA）是一种在RDF知识图谱（KG）上进行搜索的便捷方式，其中一种主流方法是将自然语言问题翻译为SPARQL查询。然而，SPARQL存在若干不足：（i）对于复杂意图和对话式问题而言较为脆弱；（ii）不适合更抽象的需求。为此，我们提出了一种新颖的双通道系统，融合了：（i）在自动从知识图谱派生的数据库上执行SQL查询所获得的结果，以及（ii）对知识图谱事实的语言化文本进行文本搜索的结果。我们的流程支持迭代检索：当任一分支的结果被认为不令人满意时，系统可以自动选择进行进一步轮次的检索。我们将所有组件整合到一个检索增强生成（RAG）设置中，由大语言模型（LLM）根据累积的搜索结果生成连贯的响应。我们在BMW汽车知识图谱上证明了所提出系统相对于多个基线的优越性。
英文摘要：
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.
