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English(EN) SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching

SemStruct框架利用PLMs和GNNs改进模式匹配

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了SemStruct，一个用于模式匹配的新框架，它结合了预训练语言模型（PLMs）的语义理解能力和图神经网络（GNNs）的结构分析能力。这种方法将表格数据建模为图，使其能够捕获在将表格视为简单文本序列时常常丢失的关键行级上下文。SemStruct在基准测试中取得了最先进的性能，甚至优于微调大型语言模型的方法，同时保持PLM冻结，仅训练一个轻量级的结构编码器。 AI

影响 通过提高模式匹配的准确性来增强数据集成，可能减少数据准备中的手动工作。

排序理由 该集群包含一篇详细介绍新模式匹配框架的学术论文。

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

SemStruct框架利用PLMs和GNNs改进模式匹配

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该集群包含一篇详细介绍新模式匹配框架的学术论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Inwon Kang, Kavitha Srinivas, Nandana Mihindukulasooriya, Sola Shirai, Parikshit Ram, Horst Samulowitz, Oshani Seneviratne ·

    SemStruct：利用结构信息进行上下文语义嵌入以实现模式匹配

    arXiv:2605.30729v1 Announce Type: new Abstract: Schema matching is a fundamental step in integrating heterogeneous data sources. While Pre-trained Language Models (PLMs) have revolutionized this task by capturing linguistic semantics, they typically process tabular data as serial…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Oshani Seneviratne ·

    SemStruct：利用结构信息进行上下文语义嵌入以实现模式匹配

    Schema matching is a fundamental step in integrating heterogeneous data sources. While Pre-trained Language Models (PLMs) have revolutionized this task by capturing linguistic semantics, they typically process tabular data as serialized text sequences of standalone column descrip…

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