Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | The Semantic Web - 16th International Conference, ESWC 2019, Proceedings |
| Herausgeber/-innen | Karl Hammar, Vanessa Lopez, Krzysztof Janowicz, Armin Haller, Miriam Fernández, Pascal Hitzler, Alasdair J.G. Gray, Amrapali Zaveri |
| Herausgeber (Verlag) | Springer Verlag |
| Seiten | 523-539 |
| Seitenumfang | 17 |
| ISBN (elektronisch) | 978-3-030-21348-0 |
| ISBN (Print) | 978-3-030-21347-3 |
| Publikationsstatus | Veröffentlicht - 2019 |
| Extern publiziert | Ja |
| Veranstaltung | 16th International Semantic Web Conference, ESWC 2019 - Portorož, Slowenien Dauer: 2 Juni 2019 → 6 Juni 2019 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 11503 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
As the availability and the inter-connectivity of RDF datasets grow, so does the necessity to understand the structure of the data. Understanding the topology of RDF graphs can guide and inform the development of, e.g. synthetic dataset generators, sampling methods, index structures, or query optimizers. In this work, we propose two resources: (i) a software framework (Resource URL of the framework: https://doi.org/10.5281/zenodo.2109469) able to acquire, prepare, and perform a graph-based analysis on the topology of large RDF graphs, and (ii) results on a graph-based analysis of 280 datasets (Resource URL of the datasets: https://doi.org/10.5281/zenodo.1214433) from the LOD Cloud with values for 28 graph measures computed with the framework. We present a preliminary analysis based on the proposed resources and point out implications for synthetic dataset generators. Finally, we identify a set of measures, that can be used to characterize graphs in the Semantic Web.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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The Semantic Web - 16th International Conference, ESWC 2019, Proceedings. Hrsg. / Karl Hammar; Vanessa Lopez; Krzysztof Janowicz; Armin Haller; Miriam Fernández; Pascal Hitzler; Alasdair J.G. Gray; Amrapali Zaveri. Springer Verlag, 2019. S. 523-539 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11503 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - A Software Framework and Datasets for the Analysis of Graph Measures on RDF Graphs
AU - Zloch, Matthäus
AU - Acosta, Maribel
AU - Hienert, Daniel
AU - Dietze, Stefan
AU - Conrad, Stefan
PY - 2019
Y1 - 2019
N2 - As the availability and the inter-connectivity of RDF datasets grow, so does the necessity to understand the structure of the data. Understanding the topology of RDF graphs can guide and inform the development of, e.g. synthetic dataset generators, sampling methods, index structures, or query optimizers. In this work, we propose two resources: (i) a software framework (Resource URL of the framework: https://doi.org/10.5281/zenodo.2109469) able to acquire, prepare, and perform a graph-based analysis on the topology of large RDF graphs, and (ii) results on a graph-based analysis of 280 datasets (Resource URL of the datasets: https://doi.org/10.5281/zenodo.1214433) from the LOD Cloud with values for 28 graph measures computed with the framework. We present a preliminary analysis based on the proposed resources and point out implications for synthetic dataset generators. Finally, we identify a set of measures, that can be used to characterize graphs in the Semantic Web.
AB - As the availability and the inter-connectivity of RDF datasets grow, so does the necessity to understand the structure of the data. Understanding the topology of RDF graphs can guide and inform the development of, e.g. synthetic dataset generators, sampling methods, index structures, or query optimizers. In this work, we propose two resources: (i) a software framework (Resource URL of the framework: https://doi.org/10.5281/zenodo.2109469) able to acquire, prepare, and perform a graph-based analysis on the topology of large RDF graphs, and (ii) results on a graph-based analysis of 280 datasets (Resource URL of the datasets: https://doi.org/10.5281/zenodo.1214433) from the LOD Cloud with values for 28 graph measures computed with the framework. We present a preliminary analysis based on the proposed resources and point out implications for synthetic dataset generators. Finally, we identify a set of measures, that can be used to characterize graphs in the Semantic Web.
UR - http://www.scopus.com/inward/record.url?scp=85066785875&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-21348-0_34
DO - 10.1007/978-3-030-21348-0_34
M3 - Conference contribution
AN - SCOPUS:85066785875
SN - 978-3-030-21347-3
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 523
EP - 539
BT - The Semantic Web - 16th International Conference, ESWC 2019, Proceedings
A2 - Hammar, Karl
A2 - Lopez, Vanessa
A2 - Janowicz, Krzysztof
A2 - Haller, Armin
A2 - Fernández, Miriam
A2 - Hitzler, Pascal
A2 - Gray, Alasdair J.G.
A2 - Zaveri, Amrapali
PB - Springer Verlag
T2 - 16th International Semantic Web Conference, ESWC 2019
Y2 - 2 June 2019 through 6 June 2019
ER -