Details
| Original language | English |
|---|---|
| Title of host publication | Semantic Intelligence |
| Subtitle of host publication | Select Proceedings of ISIC 2023 |
| Editors | Sarika Jain, Nandana Mihindukulasooriya, Valentina Janev, Cogan Matthew Shimizu |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 11-23 |
| Number of pages | 13 |
| ISBN (electronic) | 978-981-97-7356-5 |
| ISBN (print) | 9789819773558 |
| Publication status | Published - 29 Dec 2024 |
| Event | 3rd International Semantic Intelligence Conference, ISIC 2023 - Braga, Portugal Duration: 17 Oct 2023 → 19 Oct 2023 |
Publication series
| Name | Lecture Notes in Electrical Engineering |
|---|---|
| Volume | 1258 |
| ISSN (Print) | 1876-1100 |
| ISSN (electronic) | 1876-1119 |
Abstract
In recent years, the exponential growth of data has necessitated a unified schema to harmonize diverse data sources. This is where knowledge graphs (KGs) come into play. However, the creation of KGs introduces new challenges, such as handling large and heterogeneous input data and complex mappings. These challenges can lead to reduced scalability due to the significant memory consumption and extended execution times involved. We present KatanaG, a framework designed to streamline KG creation in complex scenarios, including large data sources and intricate mapping. KatanaG optimizes memory usage and execution time. When applied alongside various KG creation engines, our results indicate that KatanaG can improve the performance of these engines, by reducing execution time by up to 80% and achieve 70% memory savings.
Keywords
- Data fragmentation, Knowledge graph creation
ASJC Scopus subject areas
- Engineering(all)
- Industrial and Manufacturing Engineering
Cite this
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Semantic Intelligence : Select Proceedings of ISIC 2023. ed. / Sarika Jain; Nandana Mihindukulasooriya; Valentina Janev; Cogan Matthew Shimizu. Springer Science and Business Media Deutschland GmbH, 2024. p. 11-23 (Lecture Notes in Electrical Engineering; Vol. 1258).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Fragmenting Data Strategies to Scale Up the Knowledge Graph Creation
AU - Iglesias, Enrique
AU - Sakor, Ahmad
AU - Rohde, Philipp D.
AU - Janev, Valentina
AU - Vidal, Maria Esther
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024/12/29
Y1 - 2024/12/29
N2 - In recent years, the exponential growth of data has necessitated a unified schema to harmonize diverse data sources. This is where knowledge graphs (KGs) come into play. However, the creation of KGs introduces new challenges, such as handling large and heterogeneous input data and complex mappings. These challenges can lead to reduced scalability due to the significant memory consumption and extended execution times involved. We present KatanaG, a framework designed to streamline KG creation in complex scenarios, including large data sources and intricate mapping. KatanaG optimizes memory usage and execution time. When applied alongside various KG creation engines, our results indicate that KatanaG can improve the performance of these engines, by reducing execution time by up to 80% and achieve 70% memory savings.
AB - In recent years, the exponential growth of data has necessitated a unified schema to harmonize diverse data sources. This is where knowledge graphs (KGs) come into play. However, the creation of KGs introduces new challenges, such as handling large and heterogeneous input data and complex mappings. These challenges can lead to reduced scalability due to the significant memory consumption and extended execution times involved. We present KatanaG, a framework designed to streamline KG creation in complex scenarios, including large data sources and intricate mapping. KatanaG optimizes memory usage and execution time. When applied alongside various KG creation engines, our results indicate that KatanaG can improve the performance of these engines, by reducing execution time by up to 80% and achieve 70% memory savings.
KW - Data fragmentation
KW - Knowledge graph creation
UR - http://www.scopus.com/inward/record.url?scp=85215266783&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-7356-5_2
DO - 10.1007/978-981-97-7356-5_2
M3 - Conference contribution
AN - SCOPUS:85215266783
SN - 9789819773558
T3 - Lecture Notes in Electrical Engineering
SP - 11
EP - 23
BT - Semantic Intelligence
A2 - Jain, Sarika
A2 - Mihindukulasooriya, Nandana
A2 - Janev, Valentina
A2 - Shimizu, Cogan Matthew
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Semantic Intelligence Conference, ISIC 2023
Y2 - 17 October 2023 through 19 October 2023
ER -