Extending R2RML-F to support dynamic datatype and language tags

Research output: Contribution to journalArticlepeer-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 languageEnglish
Pages (from-to)709-716
Number of pages8
JournalProcedia Computer Science
Volume192
DOIs
Publication statusPublished - 2021
Event25th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2021 - Szczecin, Poland
Duration: 8 Sep 202110 Sep 2021

Keywords

  • Knowledge graphs
  • Linked data
  • Mapping language
  • Typed literals

Fingerprint

Dive into the research topics of 'Extending R2RML-F to support dynamic datatype and language tags'. Together they form a unique fingerprint.

Cite this