A life-cycle workflow architecture for Linked Data

Yongju Lee

doi:10.1145/3036290.3036302

A life-cycle workflow architecture for Linked Data

Yongju Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Linked Data proliferation gives rise to phenomenal growth of Open Data applications on the Web. While many standards, methods and technologies are applicable for Linked Data, there are still a number of open problems in the area of Linked Data. Particularly, there have been tremendous amounts of efforts of Semantic Web community to develop the Linked Data platform but there is still lack of a systematic approach on methodologies, technologies and mechanisms. Therefore we propose a novel Linked Data life-cycle workflow architecture and discuss a systematic overview by describing individual components in the architecture. We also outline how we are currently addressing these aspects.

Original language	English
Title of host publication	Proceedings of 2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017
Publisher	Association for Computing Machinery
Pages	117-121
Number of pages	5
ISBN (Electronic)	9781450348287
DOIs	https://doi.org/10.1145/3036290.3036302
State	Published - 13 Jan 2017
Event	2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017 - Ho Chi Minh City, Viet Nam Duration: 13 Jan 2017 → 16 Jan 2017

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017
Country/Territory	Viet Nam
City	Ho Chi Minh City
Period	13/01/17 → 16/01/17

Keywords

Graph database
Linked Data
Ontology learning method
Rdf
Sparql
Workflow architecture

Access to Document

10.1145/3036290.3036302

Cite this

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title = "A life-cycle workflow architecture for Linked Data",

abstract = "Linked Data proliferation gives rise to phenomenal growth of Open Data applications on the Web. While many standards, methods and technologies are applicable for Linked Data, there are still a number of open problems in the area of Linked Data. Particularly, there have been tremendous amounts of efforts of Semantic Web community to develop the Linked Data platform but there is still lack of a systematic approach on methodologies, technologies and mechanisms. Therefore we propose a novel Linked Data life-cycle workflow architecture and discuss a systematic overview by describing individual components in the architecture. We also outline how we are currently addressing these aspects.",

keywords = "Graph database, Linked Data, Ontology learning method, Rdf, Sparql, Workflow architecture",

author = "Yongju Lee",

note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017 ; Conference date: 13-01-2017 Through 16-01-2017",

year = "2017",

month = jan,

day = "13",

doi = "10.1145/3036290.3036302",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "117--121",

booktitle = "Proceedings of 2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017",

}

Lee, Y 2017, A life-cycle workflow architecture for Linked Data. in Proceedings of 2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 117-121, 2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017, Ho Chi Minh City, Viet Nam, 13/01/17. https://doi.org/10.1145/3036290.3036302

A life-cycle workflow architecture for Linked Data. / Lee, Yongju.
Proceedings of 2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017. Association for Computing Machinery, 2017. p. 117-121 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Y1 - 2017/1/13

N2 - Linked Data proliferation gives rise to phenomenal growth of Open Data applications on the Web. While many standards, methods and technologies are applicable for Linked Data, there are still a number of open problems in the area of Linked Data. Particularly, there have been tremendous amounts of efforts of Semantic Web community to develop the Linked Data platform but there is still lack of a systematic approach on methodologies, technologies and mechanisms. Therefore we propose a novel Linked Data life-cycle workflow architecture and discuss a systematic overview by describing individual components in the architecture. We also outline how we are currently addressing these aspects.

AB - Linked Data proliferation gives rise to phenomenal growth of Open Data applications on the Web. While many standards, methods and technologies are applicable for Linked Data, there are still a number of open problems in the area of Linked Data. Particularly, there have been tremendous amounts of efforts of Semantic Web community to develop the Linked Data platform but there is still lack of a systematic approach on methodologies, technologies and mechanisms. Therefore we propose a novel Linked Data life-cycle workflow architecture and discuss a systematic overview by describing individual components in the architecture. We also outline how we are currently addressing these aspects.

KW - Graph database

KW - Linked Data

KW - Ontology learning method

KW - Rdf

KW - Sparql

KW - Workflow architecture

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U2 - 10.1145/3036290.3036302

DO - 10.1145/3036290.3036302

M3 - Conference contribution

AN - SCOPUS:85018674203

T3 - ACM International Conference Proceeding Series

SP - 117

EP - 121

BT - Proceedings of 2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017

PB - Association for Computing Machinery

T2 - 2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017

Y2 - 13 January 2017 through 16 January 2017

ER -

A life-cycle workflow architecture for Linked Data

Abstract

Publication series

Conference

Keywords

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