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<p>In this paper, entity contextual pre-filtering is proposed to refine dataset relevance assessment and streamline data discovery. Heterogeneous Graph Neural Networks are used to exploit the local context embedded within graph-based schemas. The proposed pre-filtering approach is versatile and does not rely on any specific similarity metric, making it applicable to a wide range of data discovery methods. The proposed technique increases data discovery precision by reducing false positives and identifying significant data relationships. This method has been empirically validated across a variety of real-world datasets to improve data discovery efficiency and accuracy.</p>

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Enhancing data discovery with contextual pre-filtering

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月28日

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  • Javier Flores,
  • Sergi Nadal,
  • Oscar Romero

摘要

In this paper, entity contextual pre-filtering is proposed to refine dataset relevance assessment and streamline data discovery. Heterogeneous Graph Neural Networks are used to exploit the local context embedded within graph-based schemas. The proposed pre-filtering approach is versatile and does not rely on any specific similarity metric, making it applicable to a wide range of data discovery methods. The proposed technique increases data discovery precision by reducing false positives and identifying significant data relationships. This method has been empirically validated across a variety of real-world datasets to improve data discovery efficiency and accuracy.