DTD2OWL2: A New Approach for the Transformation of the DTD to OWL

Hacherouf, Mokhtaria; Bahloul, Safia Nait

doi:10.1016/j.procs.2015.08.506

Cited by 3 publications

(2 citation statements)

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“…Some other researchers have focused on converting to OWL ontologies the schemas of XML documents, specified using either the XML Schema language [62] or the Document Type Definition (DTD) language [60] . In particular, Bedini et al [63] have proposed a set of patterns for an automatic transformation of XML Schema files into OWL files, while Hacherouf and Bahloul [64] have studied transformation of DTD files into OWL ontologies. However, XML documents are usually complicated and verbose, whereas JSON has been designed as a lightweight alternative to XML, more suitable to big data storage and exchange.…”

Section: Related Work Discussionmentioning

confidence: 99%

$\tau\text{JOWL}$: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data

Brahmia

Grandi

Bouaziz

2022

Big Data Min. Anal.

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Nowadays, ontologies, which are defined under the OWL 2 Web Ontology Language (OWL 2), are being used in several fields like artificial intelligence, knowledge engineering, and Semantic Web environments to access data, answer queries, or infer new knowledge. In particular, ontologies can be used to model the semantics of big data as an enabling factor for the deployment of intelligent analytics. Big data are being widely stored and exchanged in JavaScript Object Notation (JSON) format, in particular by Web applications. However, JSON data collections lack explicit semantics as they are in general schema-less, which does not allow to efficiently leverage the benefits of big data. Furthermore, several applications require bookkeeping of the entire history of big data changes, for which no support is provided by mainstream Big Data management systems, including Not only SQL (NoSQL) database systems. In this paper, we propose an approach, named JOWL (temporal OWL 2 from temporal JSON), which allows users (i) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (ii) to manage its incremental maintenance accommodating the evolution of these data, in a temporal and multi-schema environment.

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Section: Related Work Discussionmentioning

confidence: 99%

$\tau\text{JOWL}$: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data

Brahmia

Grandi

Bouaziz

2022

Big Data Min. Anal.

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“…an Eclipse plugin using ATL language for transformation purposes [5], an application in which XSLT transformations are used to convert XMI files with UML models into OWL (XML format) [3], or a Protégé UML Backend plugin [9] • XML document, e.g. [4].…”

Section: Related Workmentioning

confidence: 99%

Universal Framework for OWL2 Ontology Transformations

Hnatkowska

Woroniecki²

2018

Foundations of Computing and Decision Sciences

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Domain ontologies are valuable knowledge assets with many potential applications, e.g. in software engineering. Their content is often a subject of bi-directional transformations. Unfortunately, a centralized transformation service which can be easily extended with new mappers is not available for ontology users. In consequence, they have to deal with many different translation programs, which have to be installed and learned separately. The paper presents a framework for universal ontology processing, dedicated to ontologies expressed in OWL2. The framework usefulness was verified by a proof-of-concept implementation, for which an existing OWL2 to Groovy translator was adapted. During the integration process, the translator functionality was enhanced with ontology individuals mapping. The exemplary implementation confirmed that the framework with plug-in architecture is flexible and easy for customization. The ontology stakeholders should benefit from the reduced cognitive load and more satisfying transformation process.

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