Publishing E-RDF linked data for many agents by single third-party server
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Publishing E-RDF linked data for many agents by single third-party server. / Wang, Dongsheng; Zhang, Yongyuan; Wang, Zhengjun; Chen, Tao.
Semantic Technology: 7th Joint International Conference, JIST 2017, Gold Coast, QLD, Australia, November 10-12, 2017, Proceedings. red. / Zhe Wang; Anni-Yasmin Turhan; Kewen Wang; Xiaowang Zhang. Springer, 2017. s. 151-163 (Lecture notes in computer science, Bind 10675).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Publishing E-RDF linked data for many agents by single third-party server
AU - Wang, Dongsheng
AU - Zhang, Yongyuan
AU - Wang, Zhengjun
AU - Chen, Tao
N1 - Conference code: 7
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Data integration
KW - E-RDF
KW - Knowledge representation
KW - Linked data
KW - Semantic web
KW - Web service
UR - http://www.scopus.com/inward/record.url?scp=85033791249&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-70682-5_10
DO - 10.1007/978-3-319-70682-5_10
M3 - Article in proceedings
AN - SCOPUS:85033791249
SN - 978-3-319-70681-8
T3 - Lecture notes in computer science
SP - 151
EP - 163
BT - Semantic Technology
A2 - Wang, Zhe
A2 - Turhan, Anni-Yasmin
A2 - Wang, Kewen
A2 - Zhang, Xiaowang
PB - Springer
Y2 - 10 November 2017 through 12 November 2017
ER -
ID: 188451405