Enhancing data discovery with contextual pre-filtering
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 no...
| Autores: | , , |
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| Formato: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/449055 |
| Acesso em linha: | https://hdl.handle.net/2117/449055 https://dx.doi.org/10.1186/s40537-025-01312-5 |
| Access Level: | acceso abierto |
| Palavra-chave: | Data discovery Metadata enrichment Heterogeneous graph neural networks Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació |
| Resumo: | 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. |
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