Mining governmental collaboration through semantic profiling of open data catalogues and publishers

Mohamed Adel Rezk; Adegboyega Ojo; Islam A. Hassan

doi:10.1007/978-3-319-65151-4_24

Mining governmental collaboration through semantic profiling of open data catalogues and publishers

Mohamed Adel Rezk
, Adegboyega Ojo
, Islam A. Hassan

University of Galway

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

1 Citation (Scopus)

Abstract

Due to the increasing adoption of open data among governments worldwide especially in the European Union area, a deeper analysis of the newly published data is becoming a mandate. Apart from analyzing the published dataset itself we aimed on analyzing published dataset catalogues. A dataset catalogue or a dataset metadata contains features that describe what the data is about in a textual representation. So, we first acquire data from open data portals, choose descriptive dataset catalogue features, and then construct an aggregated textual representation of the datasets. Afterwards we enrich those textual representations using Natural Language Processing (NLP) methods to create a new comparable data feature “Named Entities”. By mining the new data feature we are able to produce datasets and publishers relatedness network. Those networks are used to point similarities between the published data across multiple open data portals. Pointing all possible collaborations for integrating and standardizing data features and types would increase the value of da1ta and ease its analysis process.

Original language	English
Title of host publication	IFIP Advances in Information and Communication Technology
Editors	Luis M. Camarinha-Matos, Rosanna Fornasiero, Hamideh Afsarmanesh
Publisher	Springer New York LLC
Pages	253-264
Number of pages	12
ISBN (Print)	9783319651507
DOIs	https://doi.org/10.1007/978-3-319-65151-4_24
Publication status	Published - 2017
Externally published	Yes
Event	18th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2017 - Vicenza, Italy Duration: 18 Sep 2017 → 20 Sep 2017

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	506
ISSN (Print)	1868-4238

Conference

Conference	18th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2017
Country/Territory	Italy
City	Vicenza
Period	18/09/17 → 20/09/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

Collaborative network
Data mining
E-government
Open data
Unstructured data analysis

Access to Document

10.1007/978-3-319-65151-4_24

Cite this

Adel Rezk, M., Ojo, A., & Hassan, I. A. (2017). Mining governmental collaboration through semantic profiling of open data catalogues and publishers. In L. M. Camarinha-Matos, R. Fornasiero, & H. Afsarmanesh (Eds.), IFIP Advances in Information and Communication Technology (pp. 253-264). (IFIP Advances in Information and Communication Technology; Vol. 506). Springer New York LLC. https://doi.org/10.1007/978-3-319-65151-4_24

Adel Rezk, Mohamed ; Ojo, Adegboyega ; Hassan, Islam A. / Mining governmental collaboration through semantic profiling of open data catalogues and publishers. IFIP Advances in Information and Communication Technology. editor / Luis M. Camarinha-Matos ; Rosanna Fornasiero ; Hamideh Afsarmanesh. Springer New York LLC, 2017. pp. 253-264 (IFIP Advances in Information and Communication Technology).

@inproceedings{019390261d964e28baa6f609605e402d,

title = "Mining governmental collaboration through semantic profiling of open data catalogues and publishers",

abstract = "Due to the increasing adoption of open data among governments worldwide especially in the European Union area, a deeper analysis of the newly published data is becoming a mandate. Apart from analyzing the published dataset itself we aimed on analyzing published dataset catalogues. A dataset catalogue or a dataset metadata contains features that describe what the data is about in a textual representation. So, we first acquire data from open data portals, choose descriptive dataset catalogue features, and then construct an aggregated textual representation of the datasets. Afterwards we enrich those textual representations using Natural Language Processing (NLP) methods to create a new comparable data feature “Named Entities”. By mining the new data feature we are able to produce datasets and publishers relatedness network. Those networks are used to point similarities between the published data across multiple open data portals. Pointing all possible collaborations for integrating and standardizing data features and types would increase the value of da1ta and ease its analysis process.",

keywords = "Collaborative network, Data mining, E-government, Open data, Unstructured data analysis",

author = "\{Adel Rezk\}, Mohamed and Adegboyega Ojo and Hassan, \{Islam A.\}",

note = "Publisher Copyright: {\textcopyright} IFIP International Federation for Information Processing 2017.; 18th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2017 ; Conference date: 18-09-2017 Through 20-09-2017",

year = "2017",

doi = "10.1007/978-3-319-65151-4\_24",

language = "English",

isbn = "9783319651507",

series = "IFIP Advances in Information and Communication Technology",

publisher = "Springer New York LLC",

pages = "253--264",

editor = "Camarinha-Matos, \{Luis M.\} and Rosanna Fornasiero and Hamideh Afsarmanesh",

booktitle = "IFIP Advances in Information and Communication Technology",

}

Adel Rezk, M, Ojo, A & Hassan, IA 2017, Mining governmental collaboration through semantic profiling of open data catalogues and publishers. in LM Camarinha-Matos, R Fornasiero & H Afsarmanesh (eds), IFIP Advances in Information and Communication Technology. IFIP Advances in Information and Communication Technology, vol. 506, Springer New York LLC, pp. 253-264, 18th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2017, Vicenza, Italy, 18/09/17. https://doi.org/10.1007/978-3-319-65151-4_24

Mining governmental collaboration through semantic profiling of open data catalogues and publishers. / Adel Rezk, Mohamed; Ojo, Adegboyega; Hassan, Islam A.
IFIP Advances in Information and Communication Technology. ed. / Luis M. Camarinha-Matos; Rosanna Fornasiero; Hamideh Afsarmanesh. Springer New York LLC, 2017. p. 253-264 (IFIP Advances in Information and Communication Technology; Vol. 506).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

TY - GEN

T1 - Mining governmental collaboration through semantic profiling of open data catalogues and publishers

AU - Adel Rezk, Mohamed

AU - Ojo, Adegboyega

AU - Hassan, Islam A.

N1 - Publisher Copyright: © IFIP International Federation for Information Processing 2017.

PY - 2017

Y1 - 2017

N2 - Due to the increasing adoption of open data among governments worldwide especially in the European Union area, a deeper analysis of the newly published data is becoming a mandate. Apart from analyzing the published dataset itself we aimed on analyzing published dataset catalogues. A dataset catalogue or a dataset metadata contains features that describe what the data is about in a textual representation. So, we first acquire data from open data portals, choose descriptive dataset catalogue features, and then construct an aggregated textual representation of the datasets. Afterwards we enrich those textual representations using Natural Language Processing (NLP) methods to create a new comparable data feature “Named Entities”. By mining the new data feature we are able to produce datasets and publishers relatedness network. Those networks are used to point similarities between the published data across multiple open data portals. Pointing all possible collaborations for integrating and standardizing data features and types would increase the value of da1ta and ease its analysis process.

AB - Due to the increasing adoption of open data among governments worldwide especially in the European Union area, a deeper analysis of the newly published data is becoming a mandate. Apart from analyzing the published dataset itself we aimed on analyzing published dataset catalogues. A dataset catalogue or a dataset metadata contains features that describe what the data is about in a textual representation. So, we first acquire data from open data portals, choose descriptive dataset catalogue features, and then construct an aggregated textual representation of the datasets. Afterwards we enrich those textual representations using Natural Language Processing (NLP) methods to create a new comparable data feature “Named Entities”. By mining the new data feature we are able to produce datasets and publishers relatedness network. Those networks are used to point similarities between the published data across multiple open data portals. Pointing all possible collaborations for integrating and standardizing data features and types would increase the value of da1ta and ease its analysis process.

KW - Collaborative network

KW - Data mining

KW - E-government

KW - Open data

KW - Unstructured data analysis

UR - https://www.scopus.com/pages/publications/85029593119

U2 - 10.1007/978-3-319-65151-4_24

DO - 10.1007/978-3-319-65151-4_24

M3 - Conference Publication

AN - SCOPUS:85029593119

SN - 9783319651507

T3 - IFIP Advances in Information and Communication Technology

SP - 253

EP - 264

BT - IFIP Advances in Information and Communication Technology

A2 - Camarinha-Matos, Luis M.

A2 - Fornasiero, Rosanna

A2 - Afsarmanesh, Hamideh

PB - Springer New York LLC

T2 - 18th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2017

Y2 - 18 September 2017 through 20 September 2017

ER -

Adel Rezk M, Ojo A, Hassan IA. Mining governmental collaboration through semantic profiling of open data catalogues and publishers. In Camarinha-Matos LM, Fornasiero R, Afsarmanesh H, editors, IFIP Advances in Information and Communication Technology. Springer New York LLC. 2017. p. 253-264. (IFIP Advances in Information and Communication Technology). doi: 10.1007/978-3-319-65151-4_24

Mining governmental collaboration through semantic profiling of open data catalogues and publishers

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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