Publishing E-RDF linked data for many agents by single third-party server
(2017) 7th Joint International Conference on Semantic Technology, JIST 2017 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10675 LNCS. p.151-163- Abstract
Linked data is one of the most successful practices in semantic web, which has led to the opening and interlinking of data. Though many agents (mostly academic organizations and government) have published a large amount of linked data, numerous agents such as private companies and industries either do not have the ability or do not want to make an additional effort to publish linked data. Thus, for agents who are willing to open part of their data but do not want to make an effort, the task can be undertaken by a professional third-party server (together with professional experts) that publishes linked data for these agents. Consequently, when a single third-party server is on behalf of multiple agents, it is also responsible to... (More)
Linked data is one of the most successful practices in semantic web, which has led to the opening and interlinking of data. Though many agents (mostly academic organizations and government) have published a large amount of linked data, numerous agents such as private companies and industries either do not have the ability or do not want to make an additional effort to publish linked data. Thus, for agents who are willing to open part of their data but do not want to make an effort, the task can be undertaken by a professional third-party server (together with professional experts) that publishes linked data for these agents. Consequently, when a single third-party server is on behalf of multiple agents, it is also responsible to organize these multiple-source URIs (data) in a systematic way to make them referable, satisfying the 4-star data principles, as well as protect the confidential data of these agents. In this paper, we propose a framework to leverage these challenges and design a URI standard based on our proposed E-RDF, which extends and optimizes the existing 5-star linked data principles. Also, we introduce a customized data filtering mechanism to protect the confidential data. For validation, we implement a prototype system as a third-party server that publishes linked data for a number of agents. It demonstrates well-organized 5-star linked data plus E-RDF and shows the additional advantages of data integration and interlinking among agents.
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- author
- Wang, Dongsheng ; Zhang, Yongyuan ; Wang, Zhengjun LU and Chen, Tao
- organization
- publishing date
- 2017
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Data integration, E-RDF, Knowledge representation, Linked data, Semantic web, Web service
- host publication
- Semantic Technology - 7th Joint International Conference, JIST 2017, Proceedings
- series title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- volume
- 10675 LNCS
- pages
- 13 pages
- publisher
- Springer
- conference name
- 7th Joint International Conference on Semantic Technology, JIST 2017
- conference location
- Gold Coast, Australia
- conference dates
- 2017-11-10 - 2017-11-12
- external identifiers
-
- scopus:85033791249
- ISSN
- 0302-9743
- 1611-3349
- ISBN
- 9783319706818
- DOI
- 10.1007/978-3-319-70682-5_10
- language
- English
- LU publication?
- yes
- id
- c700999f-b1fc-4274-87bd-c75b29401789
- date added to LUP
- 2017-11-29 13:12:12
- date last changed
- 2024-03-01 10:30:32
@inproceedings{c700999f-b1fc-4274-87bd-c75b29401789,
abstract = {{<p>Linked data is one of the most successful practices in semantic web, which has led to the opening and interlinking of data. Though many agents (mostly academic organizations and government) have published a large amount of linked data, numerous agents such as private companies and industries either do not have the ability or do not want to make an additional effort to publish linked data. Thus, for agents who are willing to open part of their data but do not want to make an effort, the task can be undertaken by a professional third-party server (together with professional experts) that publishes linked data for these agents. Consequently, when a single third-party server is on behalf of multiple agents, it is also responsible to organize these multiple-source URIs (data) in a systematic way to make them referable, satisfying the 4-star data principles, as well as protect the confidential data of these agents. In this paper, we propose a framework to leverage these challenges and design a URI standard based on our proposed E-RDF, which extends and optimizes the existing 5-star linked data principles. Also, we introduce a customized data filtering mechanism to protect the confidential data. For validation, we implement a prototype system as a third-party server that publishes linked data for a number of agents. It demonstrates well-organized 5-star linked data plus E-RDF and shows the additional advantages of data integration and interlinking among agents.</p>}},
author = {{Wang, Dongsheng and Zhang, Yongyuan and Wang, Zhengjun and Chen, Tao}},
booktitle = {{Semantic Technology - 7th Joint International Conference, JIST 2017, Proceedings}},
isbn = {{9783319706818}},
issn = {{0302-9743}},
keywords = {{Data integration; E-RDF; Knowledge representation; Linked data; Semantic web; Web service}},
language = {{eng}},
pages = {{151--163}},
publisher = {{Springer}},
series = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
title = {{Publishing E-RDF linked data for many agents by single third-party server}},
url = {{http://dx.doi.org/10.1007/978-3-319-70682-5_10}},
doi = {{10.1007/978-3-319-70682-5_10}},
volume = {{10675 LNCS}},
year = {{2017}},
}