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English(EN) An Explainable Header-Centric Framework for Large-Scale Semantic Table Interpretation and Data Quality Assessment

新框架增强语义表格解释和数据质量评估能力

研究人员开发了一个以头部为中心的、可解释的框架,用于大规模语义表格的解释和数据质量评估。该系统名为HeadersIQ,利用词汇资源将列标题映射到39种可解释的类型,并保留了令牌级别的可追溯性。它能识别数据质量问题,如缺失数据、重复项和时间不匹配,并将这些问题汇总为数据源级别的质量指标。该框架在UCI和Kaggle等多个基准上进行了评估,展示了对真实世界元数据的广泛覆盖,并支持与DBpedia等知识图谱的对齐。 AI

影响 该框架通过增强表格数据的解释能力,有望提高知识图谱构建的准确性和可追溯性。

排序理由 该集群描述了一篇研究论文,详细介绍了一个用于语义表格解释和数据质量评估的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新框架增强语义表格解释和数据质量评估能力

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该集群描述了一篇研究论文,详细介绍了一个用于语义表格解释和数据质量评估的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Marcelo Valentim Silva, Hannes Herrmann, Valerie Maxville ·

    面向大规模语义表格解释和数据质量评估的可解释头部中心框架

    arXiv:2610.10541v1 Announce Type: new Abstract: Knowledge Graph (KG) quality depends not only on downstream graph validation, but also on the quality of tabular metadata used before integration. In metadata-only Semantic Table Interpretation (STI), where cell values are unavailab…