发表机构
EURECOM（欧洲电信学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究跨文本、表格和知识图谱的知识不一致性检测问题，提出\textsc{Kontrast}框架，通过文本到SPARQL和语言模型推理比较并分类不一致性，实验表明跨模态不一致性常见且有价值，为知识审计提供工具并建立基准。
AI 中文摘要
维基百科和维基数据被广泛用于信息获取、语言模型预训练和检索增强生成。它们的知识紧密相连但分散于文本、表格和知识图谱中。本文研究模态级不一致性检测问题，首先引入跨模态知识不一致性分类，涵盖信息粒度差异、直接冲突、时间变化和知识图谱不完整性。接着提出自动框架\textsc{Kontrast}，利用文本到SPARQL和语言模型推理比较基于表格的答案与知识图谱证据并分类不一致性。在多个表格问答数据集上的实验表明跨模态不一致性常见且有信息价值，揭示了真实知识冲突、知识图谱结构缺失和时间不匹配等问题，同时受文本到SPARQL错误和噪声限制。分析表明文本、表格和知识图谱可通过系统比较相互补充和修正。\textsc{Kontrast}为大规模知识审计提供实用工具并为跨模态知识一致性未来工作建立基准。代码和数据可通过指定链接获取。
英文摘要
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 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 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. 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.
