TY - JOUR
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 - http://www.scopus.com/inward/record.url?scp=85116895950&partnerID=8YFLogxK
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 -