Details
| Original language | English |
|---|---|
| Title of host publication | SAC '24 |
| Subtitle of host publication | Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing |
| Pages | 1668-1670 |
| Number of pages | 3 |
| ISBN (electronic) | 9798400702433 |
| Publication status | Published - 21 May 2024 |
| Event | 39th Annual ACM Symposium on Applied Computing, SAC 2024 - Avila, Spain Duration: 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 subject areas
- Computer Science(all)
- Software
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SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing. 2024. p. 1668-1670.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - KGSaw
T2 - 39th Annual ACM Symposium on Applied Computing, SAC 2024
AU - Iglesias, Enrique
AU - Vidal, Maria Esther
N1 - Publisher Copyright: © 2024 Copyright held by the owner/author(s).
PY - 2024/5/21
Y1 - 2024/5/21
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85197680335&partnerID=8YFLogxK
U2 - 10.1145/3605098.3636186
DO - 10.1145/3605098.3636186
M3 - Conference contribution
AN - SCOPUS:85197680335
SP - 1668
EP - 1670
BT - SAC '24
Y2 - 8 April 2024 through 12 April 2024
ER -