Constrained Entity Selection under Partial Knowledge for LLM-Based Knowledge Graph QA

论文概要

研究领域: AI
作者: Emanuel Kitzelmann
发布时间: 2026-08-25
arXiv: 2608.24824

中文摘要

大语言模型越来越多地用于知识图谱问答（KGQA），但可能无法正确将答案基于底层图谱。当前LLM-based KGQA方法要么依赖完整的语义解析为可执行查询（如SPARQL），这在实践中由于复杂模式或现实世界KG的不完整性而脆弱，要么依赖KG上的LLM推理和答案生成，这可能更鲁棒但缺乏形式保证。在本工作中，我们研究一种互补设置，其中候选答案由基于LLM的系统生成，然后使用从问题派生的轻量级符号约束进行验证。我们引入部分知识下的约束实体选择（CES-PK），一种专注于消除无效答案并为有效答案提供符号支持而无需构建可执行逻辑形式的问题形式化。为考虑不完整的KG，我们采用三值约束语义（满足、违反、未知），在开放世界假设下避免错误拒绝。

原文摘要

Large language models are increasingly used for knowledge graph question answering (KGQA), but can fail to correctly ground answers in the underlying graph. Current approaches to LLM-based KGQA either rely on full semantic parsing into executable queries such as SPARQL, which is brittle in practice due to complex schemas or incompleteness of real-world KGs, or on LLM-reasoning and answer generation over KGs, which can be more robust but lacks formal guarantees. In this work, we study a complementary setting in which \emph{candidate} answers are generated by an LLM-based system and subsequently verified using lightweight symbolic constraints derived from the question. We introduce \emph{Constrained Entity Selection under Partial Knowledge (CES-PK)}, a problem formulation that focuses on eli...

自动采集于 2026-08-27

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