Tobias Grubenmann
Decentralizing the Semantic Web: Who Will Pay to Realize It?
Grubenmann, Tobias; Dell'Aglio, Daniele; Bernstein, Abraham; Moor, Dmitry; Seuken, Sven
Authors
Daniele Dell'Aglio
Abraham Bernstein
Dmitry Moor
Sven Seuken
Abstract
Fueled by enthusiasm of volunteers, government subsidies, and open data legislation, the Web of Data (WoD) has enjoyed a phenomenal growth. Commercial data, however, has been stuck in proprietary silos, as the monetization strategy for sharing data in the WoD is unclear. This contrasts with the traditional web where advertisement fueled a lot of the growth. This raises the question how the WoD can (i) maintain its success when government subsidies disappear and (ii) convince commercial entities to share their wealth of data. In this paper, we propose a marketplace for decentralized data following basic WoD principles. Our approach allows a customer to buy data from different, decentralized providers in a transparent way. As such, our marketplace presents a first step towards an economically viable WoD beyond subsidies.
Citation
Grubenmann, T., Dell'Aglio, D., Bernstein, A., Moor, D., & Seuken, S. (2017). Decentralizing the Semantic Web: Who Will Pay to Realize It?. In Proceedings of the Workshop on Decentralizing the Semantic Web 2017 co-located with 16th International Semantic Web Conference (ISWC 2017)
Conference Name | DeSemWeb 2017: Decentralizing the Semantic Web |
---|---|
Conference Location | Vienna |
Start Date | Oct 22, 2017 |
End Date | Oct 22, 2017 |
Publication Date | 2017 |
Deposit Date | Jun 3, 2023 |
Publicly Available Date | Jun 6, 2023 |
Publisher | CEUR Workshop Proceedings |
Volume | 1934 |
Series ISSN | 1613-0073 |
Book Title | Proceedings of the Workshop on Decentralizing the Semantic Web 2017 co-located with 16th International Semantic Web Conference (ISWC 2017) |
Chapter Number | 1 |
Publisher URL | https://ceur-ws.org/Vol-1934/contribution-01.pdf |
Files
Decentralizing The Semantic Web: Who Will Pay To Realize It?
(340 Kb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
You might also like
Modeling Long-Range Travelling Times with Big Railway Data
(2022)
Conference Proceeding
DeepTEA: effective and efficient online time-dependent trajectory outlier detection
(2022)
Journal Article
Leveraging Contextual Graphs for Stochastic Weight Completion in Sparse Road Networks
(2022)
Conference Proceeding
Spatial concept learning and inference on geospatial polygon data
(2022)
Journal Article
A framework for differentially-private knowledge graph embeddings
(2021)
Journal Article
Downloadable Citations
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search