The Method of Normalizing OWL 2 DL Ontologies

Malgorzata Sadowska; Zbigniew Huzar

The Method of Normalizing OWL 2 DL Ontologies

Malgorzata Sadowska * §

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Zbigniew Huzar

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Wroclaw University of Science and Technology

The Method of Normalizing OWL 2 DL Ontologies

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Abstract

The paper proposes a method of normalizing OWL 2 DL ontologies. The method introduces rules aimed at refactoring OWL 2 constructs. The proposed transformations only use a subset of OWL 2 constructs and enable to present an input OWL 2 ontology in a new but semantically equivalent form. The normalization is motivated by the fact that normalized OWL 2 DL ontologies have a unified structure of axioms so that they can be compared in an algorithmic way.

References

13 Cites in Article

Reference Format

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Malgorzata Sadowska, Malgorzata Sadowska, Zbigniew Huzar. 2018. "The Method of Normalizing OWL 2 DL Ontologies". Global Journal of Computer Science and Technology, Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 18 (GJCST Volume 18 Issue C2).

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GJCST Volume 18 Issue C2

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-C Classification H.3.5

Submission ReceivedDecember 13, 2017
Peer Review Double Blind
Handling Editor
Accepted January 3, 2018
Published January 15, 2018

Version of record

v1.2

Issue date

May 25, 2018

Language

English

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Article Matrices

Total Score: 162

Country: Poland

Subject: Global Journal of Computer Science and Technology

Authors: Malgorzata Sadowska, Zbigniew Huzar (PhD/Dr. count: 0)

View Count (all-time): 393

Total Views (Real + Logic): 2199

Total Downloads (simulated): 191

Publish Date: 2018 01, Mon

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Total Views: 2.2K

Total Downloads: 191

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