KGSaw: One Size Does Not Fit All- Planning Methods for Data Fragmentation for Efficiently Creating Knowledge Graphs

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

Autoren

  • Enrique Iglesias
  • Maria Esther Vidal

Organisationseinheiten

Externe Organisationen

  • Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des SammelwerksSAC '24
UntertitelProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing
Seiten1668-1670
Seitenumfang3
ISBN (elektronisch)9798400702433
PublikationsstatusVeröffentlicht - 21 Mai 2024
Veranstaltung39th Annual ACM Symposium on Applied Computing, SAC 2024 - Avila, Spanien
Dauer: 8 Apr. 2024 → 12 Apr. 2024

Abstract

This research addresses the challenges in planning knowledge graph (KG) creation. It presents KGSaw for partitioning and integrating data sources leveraging functional dependencies, while minimizing memory usage and execution time. Experimental results, involving existing KG creation engines, demonstrate KGSaw ability to enhance efficiency, with memory reductions up to 121.34 times and execution time improvements by a factor of 84.59. This emphasizes the importance of considering data source characteristics, like functional dependencies, in KG creation planning.

ASJC Scopus Sachgebiete

Zitieren

KGSaw: One Size Does Not Fit All- Planning Methods for Data Fragmentation for Efficiently Creating Knowledge Graphs. / Iglesias, Enrique; Vidal, Maria Esther.
SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing. 2024. S. 1668-1670.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

Iglesias, E & Vidal, ME 2024, KGSaw: One Size Does Not Fit All- Planning Methods for Data Fragmentation for Efficiently Creating Knowledge Graphs. in SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing. S. 1668-1670, 39th Annual ACM Symposium on Applied Computing, SAC 2024, Avila, Spanien, 8 Apr. 2024. https://doi.org/10.1145/3605098.3636186
Iglesias, E., & Vidal, M. E. (2024). KGSaw: One Size Does Not Fit All- Planning Methods for Data Fragmentation for Efficiently Creating Knowledge Graphs. In SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing (S. 1668-1670) https://doi.org/10.1145/3605098.3636186
Iglesias E, Vidal ME. KGSaw: One Size Does Not Fit All- Planning Methods for Data Fragmentation for Efficiently Creating Knowledge Graphs. in SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing. 2024. S. 1668-1670 doi: 10.1145/3605098.3636186
Iglesias, Enrique ; Vidal, Maria Esther. / KGSaw : One Size Does Not Fit All- Planning Methods for Data Fragmentation for Efficiently Creating Knowledge Graphs. SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing. 2024. S. 1668-1670
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