Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Knowledge Graph Construction |
| Untertitel | Proceedings of the 4th International Workshop on Knowledge Graph Construction co-located with 20th Extended Semantic Web Conference |
| Seitenumfang | 8 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | 4th International Workshop on Knowledge Graph Construction, KGCW 2023 - Hersonissos, Griechenland Dauer: 28 Mai 2023 → 28 Mai 2023 |
Publikationsreihe
| Name | CEUR Workshop Proceedings |
|---|---|
| Herausgeber (Verlag) | CEUR Workshop Proceedings |
| Band | 3471 |
| ISSN (Print) | 1613-0073 |
Abstract
The amount of data being generated in recent years has increased drastically. Thus, a unified schema must be defined to bring multiple data sources into a single format. For that reason, the use of knowledge graphs has become much more commonplace. When creating a knowledge graph, different parameters affect the creation process, like the size and heterogeneity of the input data and the complexity of the input mapping. Multiple knowledge graph creation engines have been developed that handle these parameters differently. Therefore, a benchmark is needed to be defined to evaluate the performance of these engines. KGCW 2023 Challenge dataset presents a wide array of test cases to discover each engine's strengths and weaknesses and determine which engine is best suited for each case. This work reports the results of evaluating the performance of SDM-RDFizer while using this dataset.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Knowledge Graph Construction: Proceedings of the 4th International Workshop on Knowledge Graph Construction co-located with 20th Extended Semantic Web Conference. 2023. (CEUR Workshop Proceedings; Band 3471).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Knowledge Graph Creation Challenge
T2 - 4th International Workshop on Knowledge Graph Construction, KGCW 2023
AU - Iglesias, Enrique
AU - Vidal, Maria Esther
N1 - Funding Information: This work has been partially supported by the Federal Ministry for Economic Affairs and Energy of Germany ?BMWK) in the project CoyPu ?project number 01MK21007[A-L]). Leibniz Association partially funds Maria-Esther Vidal in the ”Leibniz Best Minds: Programme for Women Professors”, project TrustKG-Transforming Data in Trustable Insights with grant P99/2020.
PY - 2023
Y1 - 2023
N2 - The amount of data being generated in recent years has increased drastically. Thus, a unified schema must be defined to bring multiple data sources into a single format. For that reason, the use of knowledge graphs has become much more commonplace. When creating a knowledge graph, different parameters affect the creation process, like the size and heterogeneity of the input data and the complexity of the input mapping. Multiple knowledge graph creation engines have been developed that handle these parameters differently. Therefore, a benchmark is needed to be defined to evaluate the performance of these engines. KGCW 2023 Challenge dataset presents a wide array of test cases to discover each engine's strengths and weaknesses and determine which engine is best suited for each case. This work reports the results of evaluating the performance of SDM-RDFizer while using this dataset.
AB - The amount of data being generated in recent years has increased drastically. Thus, a unified schema must be defined to bring multiple data sources into a single format. For that reason, the use of knowledge graphs has become much more commonplace. When creating a knowledge graph, different parameters affect the creation process, like the size and heterogeneity of the input data and the complexity of the input mapping. Multiple knowledge graph creation engines have been developed that handle these parameters differently. Therefore, a benchmark is needed to be defined to evaluate the performance of these engines. KGCW 2023 Challenge dataset presents a wide array of test cases to discover each engine's strengths and weaknesses and determine which engine is best suited for each case. This work reports the results of evaluating the performance of SDM-RDFizer while using this dataset.
KW - Data Integration System
KW - Knowledge Graph Creation
KW - RDF Mapping Languages
UR - http://www.scopus.com/inward/record.url?scp=85173544700&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85173544700
T3 - CEUR Workshop Proceedings
BT - Knowledge Graph Construction
Y2 - 28 May 2023 through 28 May 2023
ER -