ARDI: Automatic Generation of RDFS Models from Heterogeneous Data Sources

Shumet Tadesse Nigatu; Cristina Gomez; Oscar Romero; Katja Hose; Kashif Rabbani

doi:10.1109/EDOC.2019.00031

ARDI: Automatic Generation of RDFS Models from Heterogeneous Data Sources

Shumet Tadesse Nigatu, Cristina Gomez, Oscar Romero, Katja Hose, Kashif Rabbani

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

2 Citations (Scopus)

Abstract

The current wealth of information, typically known as Big Data, generates a large amount of available data for organisations. Data Integration provides foundations to query disparate data sources as if they were integrated into a single source. However, current data integration tools are far from being useful for most organisations due to the heterogeneous nature of data sources, which represents a challenge for current frameworks. To enable data integration of highly heterogeneous and disparate data sources, this paper proposes a method to extract the schema from semi-structured (such as JSON and XML) and structured (such as relational) data sources, and generate an equivalent RDFS representation. The output of our method complements current frameworks and reduces the manual workload required to represent the input data sources in terms of the integration canonical data model. Our approach consists of production rules at the meta-model level that guarantee the correctness of the model translations. Finally, a tool for implementing our approach has been developed.

Original language	English
Title of host publication	2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC)
Number of pages	7
Publisher	IEEE Press
Publication date	2019
Pages	190-196
Article number	8945018
ISBN (Electronic)	978-1-7281-2702-6
DOIs	https://doi.org/10.1109/EDOC.2019.00031
Publication status	Published - 2019
Event	23rd IEEE International EDOC Conference - The Enterprise Computing Conference - Paris, France Duration: 28 Oct 2019 → 31 Oct 2019 https://edoc2019.sciencesconf.org/

Conference

Conference	23rd IEEE International EDOC Conference - The Enterprise Computing Conference
Country/Territory	France
City	Paris
Period	28/10/2019 → 31/10/2019
Internet address	https://edoc2019.sciencesconf.org/

Series	IEEE International Enterprise Distributed Object Computing Conference (EDOC)
ISSN	2325-6362

Keywords

Data Integration
Data Model Translation
Meta-modeling
RDF Schema

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10.1109/EDOC.2019.00031

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Cite this

@inproceedings{5ba95723ae8d4132a3d1fa9419725daf,

title = "ARDI: Automatic Generation of RDFS Models from Heterogeneous Data Sources",

abstract = "The current wealth of information, typically known as Big Data, generates a large amount of available data for organisations. Data Integration provides foundations to query disparate data sources as if they were integrated into a single source. However, current data integration tools are far from being useful for most organisations due to the heterogeneous nature of data sources, which represents a challenge for current frameworks. To enable data integration of highly heterogeneous and disparate data sources, this paper proposes a method to extract the schema from semi-structured (such as JSON and XML) and structured (such as relational) data sources, and generate an equivalent RDFS representation. The output of our method complements current frameworks and reduces the manual workload required to represent the input data sources in terms of the integration canonical data model. Our approach consists of production rules at the meta-model level that guarantee the correctness of the model translations. Finally, a tool for implementing our approach has been developed.",

keywords = "Data Integration, Data Model Translation, Meta-modeling, RDF Schema",

author = "Nigatu, {Shumet Tadesse} and Cristina Gomez and Oscar Romero and Katja Hose and Kashif Rabbani",

year = "2019",

doi = "10.1109/EDOC.2019.00031",

language = "English",

series = "IEEE International Enterprise Distributed Object Computing Conference (EDOC)",

publisher = "IEEE Press",

pages = "190--196",

booktitle = "2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC)",

note = "23rd IEEE International EDOC Conference - The Enterprise Computing Conference ; Conference date: 28-10-2019 Through 31-10-2019",

url = "https://edoc2019.sciencesconf.org/",

}

Nigatu, ST, Gomez, C, Romero, O, Hose, K & Rabbani, K 2019, ARDI: Automatic Generation of RDFS Models from Heterogeneous Data Sources. in 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC)., 8945018, IEEE Press, IEEE International Enterprise Distributed Object Computing Conference (EDOC), pp. 190-196, 23rd IEEE International EDOC Conference - The Enterprise Computing Conference, Paris, France, 28/10/2019. https://doi.org/10.1109/EDOC.2019.00031

ARDI: Automatic Generation of RDFS Models from Heterogeneous Data Sources. / Nigatu, Shumet Tadesse; Gomez, Cristina; Romero, Oscar et al.
2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC). IEEE Press, 2019. p. 190-196 8945018 (IEEE International Enterprise Distributed Object Computing Conference (EDOC)).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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T1 - ARDI

T2 - 23rd IEEE International EDOC Conference - The Enterprise Computing Conference

AU - Nigatu, Shumet Tadesse

AU - Gomez, Cristina

AU - Romero, Oscar

AU - Hose, Katja

AU - Rabbani, Kashif

PY - 2019

Y1 - 2019

N2 - The current wealth of information, typically known as Big Data, generates a large amount of available data for organisations. Data Integration provides foundations to query disparate data sources as if they were integrated into a single source. However, current data integration tools are far from being useful for most organisations due to the heterogeneous nature of data sources, which represents a challenge for current frameworks. To enable data integration of highly heterogeneous and disparate data sources, this paper proposes a method to extract the schema from semi-structured (such as JSON and XML) and structured (such as relational) data sources, and generate an equivalent RDFS representation. The output of our method complements current frameworks and reduces the manual workload required to represent the input data sources in terms of the integration canonical data model. Our approach consists of production rules at the meta-model level that guarantee the correctness of the model translations. Finally, a tool for implementing our approach has been developed.

AB - The current wealth of information, typically known as Big Data, generates a large amount of available data for organisations. Data Integration provides foundations to query disparate data sources as if they were integrated into a single source. However, current data integration tools are far from being useful for most organisations due to the heterogeneous nature of data sources, which represents a challenge for current frameworks. To enable data integration of highly heterogeneous and disparate data sources, this paper proposes a method to extract the schema from semi-structured (such as JSON and XML) and structured (such as relational) data sources, and generate an equivalent RDFS representation. The output of our method complements current frameworks and reduces the manual workload required to represent the input data sources in terms of the integration canonical data model. Our approach consists of production rules at the meta-model level that guarantee the correctness of the model translations. Finally, a tool for implementing our approach has been developed.

KW - Data Integration

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KW - Meta-modeling

KW - RDF Schema

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M3 - Article in proceeding

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EP - 196

BT - 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC)

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ER -

ARDI: Automatic Generation of RDFS Models from Heterogeneous Data Sources

Abstract

Conference

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Poul Due Jensen Professorate in Big Data and Artificial Intelligence

IT4BI-DC: Erasmus Mundus Joint Doctorate in Information Technologies for Business Intelligence – Doctoral College

Cite this

ARDI: Automatic Generation of RDFS Models from Heterogeneous Data Sources

Abstract

Conference

Keywords

Access to Document

AUB Link

Other files and links

Fingerprint

Projects

Poul Due Jensen Professorate in Big Data and Artificial Intelligence

IT4BI-DC: Erasmus Mundus Joint Doctorate in Information Technologies for Business Intelligence – Doctoral College

Cite this