用数据库和事实文本双路检索提升对话问答准确率
RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG
- 从知识图谱自动构建数据库，结合文本搜索实现双路检索
- 支持多轮迭代，结果不理想时自动触发下一轮检索
- 在宝马汽车知识图谱上优于多个基线方法
对话式问答（ConvQA）是查询RDF知识图谱的便捷方式，现有方法通常将自然语言问题转化为SPARQL查询。然而SPARQL存在两个缺陷：（i）对复杂意图和对话式问题脆弱；（ii）不适用于更抽象的需求。为此，我们提出一种新型双路径系统：融合（i）从知识图谱自动生成的数据库上的SQL查询结果，以及（ii）知识图谱事实的文本化表示上的文本搜索结果。该流程支持多轮迭代检索：当任一路径的结果不理想时，系统可自动发起新一轮检索。所有结果整合进检索增强生成（RAG）框架，由大语言模型生成连贯回答。我们在宝马汽车知识图谱上验证了该系统相较于多个基线的优越性。
原文摘要 · Abstract (English)
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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