想系统理解文本、表格和知识图谱为什么打架？看看这篇论文，它用SPARQL和LLM自动找出不一致，还分了四种类型，挺实用的。
Kontrast是一个基于Text-to-SPARQL和LLM推理的自动框架，用于检测Wikipedia和Wikidata中文本、表格和知识图谱三种模态间的知识不一致性。论文首先提出跨模态知识不一致的分类学，涵盖信息粒度差异、直接冲突、时间变化和知识图谱不完整四种类型。在多个Table-QA数据集上的实验表明，跨模态不一致普遍存在，能揭示真正的知识冲突、缺失的KG结构及时序不匹配。Kontrast为大规模知识审计提供了实用工具，并建立了跨模态知识一致性的基准。
Detecting Knowledge Inconsistencies Across Text, Tables, and Knowledge Graphs
Wikipedia and Wikidata are widely used for information access, LLM pre-training, and retrieval-augmented generation. Their knowledge is deeply connected but scattered across text, tables, and knowledge graphs. This raises a practical question: when these modalities disagree, how can we detect and explain the conflict? We study this problem as \emph{modality-level inconsistency detection}. We first introduce a taxonomy of cross-modal knowledge inconsistencies, covering information granularity differences, direct conflicts, temporal changes, and KG incompleteness. We then present \textsc{Kontrast}, an automatic framework that uses Text-to-SPARQL and LLM reasoning to compare table-based answers with KG evidence and categorize the resulting inconsistencies. Experiments on various Table-QA datasets show that cross-modal inconsistencies are common and informative. They reveal not only true knowledge conflicts, but also missing KG structure and temporal mismatches while being limited by Text-to-SPARQL errors and noise. Our analysis shows that text, tables, and KGs can complement and correct one another through systematic comparison. \textsc{Kontrast} provides a practical tool for large-scale knowledge auditing and establishes a benchmark for future work on cross-modal knowledge consistency. Code and data are available at https://github.com/ECLADATTA/KONTRAST.