τJOWL: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data
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...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2022-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2021.9020019 |
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| author | Zouhaier Brahmia Fabio Grandi Rafik Bouaziz |
| author_facet | Zouhaier Brahmia Fabio Grandi Rafik Bouaziz |
| author_sort | Zouhaier Brahmia |
| collection | DOAJ |
| description | 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 τJOWL (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. |
| format | Article |
| id | doaj-art-b530750c09a24cc298361f5cef990289 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-b530750c09a24cc298361f5cef9902892025-02-02T06:14:04ZengTsinghua University PressBig Data Mining and Analytics2096-06542022-12-015427128110.26599/BDMA.2021.9020019τJOWL: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big DataZouhaier Brahmia0Fabio Grandi1Rafik Bouaziz2Faculty of Economics and Management, University of Sfax, Sfax 3029, TunisiaDepartment of Computer Science and Engineering, University of Bologna, Bologna 40136, ItalyFaculty of Economics and Management, University of Sfax, Sfax 3029, TunisiaNowadays, 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 τJOWL (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.https://www.sciopen.com/article/10.26599/BDMA.2021.9020019big datajavascript object notation (json)json schematemporal jsonontologytemporal ontologyτjschemaτowl |
| spellingShingle | Zouhaier Brahmia Fabio Grandi Rafik Bouaziz τJOWL: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data Big Data Mining and Analytics big data javascript object notation (json) json schema temporal json ontology temporal ontology τjschema τowl |
| title | τJOWL: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data |
| title_full | τJOWL: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data |
| title_fullStr | τJOWL: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data |
| title_full_unstemmed | τJOWL: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data |
| title_short | τJOWL: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data |
| title_sort | τjowl a systematic approach to build and evolve a temporal owl 2 ontology based on temporal json big data |
| topic | big data javascript object notation (json) json schema temporal json ontology temporal ontology τjschema τowl |
| url | https://www.sciopen.com/article/10.26599/BDMA.2021.9020019 |
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