Extending R2RML-F to support dynamic datatype and language tags

Aparna Nayak; Bojan Božic; Luca Longo

doi:10.1016/j.procs.2021.08.073

Extending R2RML-F to support dynamic datatype and language tags

Aparna Nayak
, Bojan Božic
, Luca Longo

Technological University Dublin

Research output: Contribution to journal › Article › peer-review

Abstract

Linked data is often generated from raw data with the help of mapping languages. Complex data transformation is one of the essential parts while uplifting data which either can be implemented as custom solutions or separated from the mapping process. In this paper, we propose an approach of separating complex data transformations from the mapping process that can still be reusable across the systems. In the proposed method, complex data transformations include the entailment of (i) language tag and (ii) datatype present at the data source. The proposed method also includes inferring missing datatype information. We extended R2RML-F to handle data transformations. The results showed that transformation functions could be used to create typed literals dynamically. Our approach is validated on the test cases specified by the RDF mapping language (RML). The proposed method considers data in the form of JSON, thus making the system interoperable and reusable.

Original language	English
Pages (from-to)	709-716
Number of pages	8
Journal	Procedia Computer Science
Volume	192
DOIs	https://doi.org/10.1016/j.procs.2021.08.073
Publication status	Published - 2021
Externally published	Yes
Event	25th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2021 - Szczecin, Poland Duration: 8 Sep 2021 → 10 Sep 2021

Keywords

Knowledge graphs
Linked data
Mapping language
Typed literals

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10.1016/j.procs.2021.08.073

Cite this

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title = "Extending R2RML-F to support dynamic datatype and language tags",

abstract = "Linked data is often generated from raw data with the help of mapping languages. Complex data transformation is one of the essential parts while uplifting data which either can be implemented as custom solutions or separated from the mapping process. In this paper, we propose an approach of separating complex data transformations from the mapping process that can still be reusable across the systems. In the proposed method, complex data transformations include the entailment of (i) language tag and (ii) datatype present at the data source. The proposed method also includes inferring missing datatype information. We extended R2RML-F to handle data transformations. The results showed that transformation functions could be used to create typed literals dynamically. Our approach is validated on the test cases specified by the RDF mapping language (RML). The proposed method considers data in the form of JSON, thus making the system interoperable and reusable.",

keywords = "Knowledge graphs, Linked data, Mapping language, Typed literals",

author = "Aparna Nayak and Bojan Bo{\v z}ic and Luca Longo",

note = "Publisher Copyright: {\textcopyright} 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.; 25th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2021 ; Conference date: 08-09-2021 Through 10-09-2021",

year = "2021",

doi = "10.1016/j.procs.2021.08.073",

language = "English",

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pages = "709--716",

journal = "Procedia Computer Science",

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T1 - Extending R2RML-F to support dynamic datatype and language tags

AU - Nayak, Aparna

AU - Božic, Bojan

AU - Longo, Luca

N1 - Publisher Copyright: © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.

PY - 2021

Y1 - 2021

N2 - Linked data is often generated from raw data with the help of mapping languages. Complex data transformation is one of the essential parts while uplifting data which either can be implemented as custom solutions or separated from the mapping process. In this paper, we propose an approach of separating complex data transformations from the mapping process that can still be reusable across the systems. In the proposed method, complex data transformations include the entailment of (i) language tag and (ii) datatype present at the data source. The proposed method also includes inferring missing datatype information. We extended R2RML-F to handle data transformations. The results showed that transformation functions could be used to create typed literals dynamically. Our approach is validated on the test cases specified by the RDF mapping language (RML). The proposed method considers data in the form of JSON, thus making the system interoperable and reusable.

AB - Linked data is often generated from raw data with the help of mapping languages. Complex data transformation is one of the essential parts while uplifting data which either can be implemented as custom solutions or separated from the mapping process. In this paper, we propose an approach of separating complex data transformations from the mapping process that can still be reusable across the systems. In the proposed method, complex data transformations include the entailment of (i) language tag and (ii) datatype present at the data source. The proposed method also includes inferring missing datatype information. We extended R2RML-F to handle data transformations. The results showed that transformation functions could be used to create typed literals dynamically. Our approach is validated on the test cases specified by the RDF mapping language (RML). The proposed method considers data in the form of JSON, thus making the system interoperable and reusable.

KW - Knowledge graphs

KW - Linked data

KW - Mapping language

KW - Typed literals

UR - https://www.scopus.com/pages/publications/85116895950

U2 - 10.1016/j.procs.2021.08.073

DO - 10.1016/j.procs.2021.08.073

M3 - Article

AN - SCOPUS:85116895950

SN - 1877-0509

VL - 192

SP - 709

EP - 716

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 25th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2021

Y2 - 8 September 2021 through 10 September 2021

ER -

Extending R2RML-F to support dynamic datatype and language tags

Abstract

Keywords

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