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

Brahmia, Zouhaier; Grandi, Fábio; Bouaziz, Rafik

doi:10.26599/bdma.2021.9020019

Cited by 6 publications

(12 citation statements)

References 47 publications

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“…In this paper, we have proposed, for the JOWL framework [29] , a TSQL2-like temporal ontology query language, named SQWRL. SQWRL has been defined as a valid-time extension to the SQWRL query language.…”

Section: Discussionmentioning

confidence: 99%

“…The SQWRL-Processor will be integrated in the JOWL-Manager system that is being developed to support the whole JOWL framework [29] . The SQWRL-Processor is a temporal stratum [48] , written in Java, programmed to run on top of a traditional (i.e., nontemporal) SQWRL engine (like the SWRLTab plugin [34] developed in Protégé-OWL), as shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Recall that, in the JOWL framework, ontology schemas are versioned along transaction-time [29] ; for that reason, each ontology schema version has only a transaction-time timestamp.…”

Section: Syntax and Semantics Of Sqwrlmentioning

confidence: 99%

“…From one hand, the semantics of big data [26] is very helpful for both big data management [27] and analytics [28] , and, from the other hand, ontologies are widely used to formally represent data semantics of applications. Hence, we have proposed in a previous work [29] an approach, named JOWL (temporal OWL 2 from temporal JSON, where OWK 2 stands for OWL 2 Web Ontology Language), which allows (1) to automatically produce a temporal OWL 2 [30] ontology of data, following the Closed World Assumption (CWA) [31] , starting from temporal JSON-based big data, and (2) to manage the incremental maintenance of such a temporal ontology by accommodating the evolution of temporal big data, in a temporal and multi-schema-version environment. Notice that OWL 2 is the World Wide Web Consortium (W3C) standard ontology language for the Semantic Web ontologies.…”

Section: Introduction 1context Of Workmentioning

confidence: 99%

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τSQWRL: A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data

Brahmia

Grandi

Bouaziz

2023

Big Data Min. Anal.

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Temporal ontologies allow to represent not only concepts, their properties, and their relationships, but also time-varying information through explicit versioning of definitions or through the four-dimensional perdurantist view.They are widely used to formally represent temporal data semantics in several applications belonging to different fields (e.g., Semantic Web, expert systems, knowledge bases, big data, and artificial intelligence). They facilitate temporal knowledge representation and discovery, with the support of temporal data querying and reasoning. However, there is no standard or consensual temporal ontology query language. In a previous work, we have proposed an approach named JOWL (temporal OWL 2 from temporal JSON, where OWL 2 stands for "OWL 2 Web Ontology Language" and JSON stands for "JavaScript Object Notation"). JOWL allows (1) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (2) to manage its incremental maintenance accommodating their evolution, in a temporal and multi-schema-version environment. In this paper, we propose a temporal ontology query language for JOWL , named SQWRL (temporal SQWRL), designed as a temporal extension of the ontology query language-Semantic Query-enhanced Web Rule Language (SQWRL). The new language has been inspired by the features of the consensual temporal query language TSQL2 (Temporal SQL2), well known in the temporal (relational) database community. The aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any (complex) temporal query on time-varying ontologies generated from time-varying big data. Some examples, in the Internet of Healthcare Things (IoHT) domain, are provided to motivate and illustrate our proposal.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Recall that, in the JOWL framework, ontology schemas are versioned along transaction-time [29] ; for that reason, each ontology schema version has only a transaction-time timestamp.…”

Section: Syntax and Semantics Of Sqwrlmentioning

confidence: 99%

Section: Introduction 1context Of Workmentioning

confidence: 99%

See 2 more Smart Citations

τSQWRL: A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data

Brahmia

Grandi

Bouaziz

2023

Big Data Min. Anal.

Self Cite

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“…• Baseline Models: KG [2], TOTJ [6], and CQAS [12] were selected as baseline models for comparative analysis.…”

Section: Experimental Setup Parametersmentioning

confidence: 99%

Enhancing Agricultural Knowledge Management using an efficient & Novel Ontology-Based Approach Leveraging BERT-GPT and Graph Recurrent Q Learning Network

2024

Journal of Electrical Systems

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Agriculture, as one of the key drivers for human civilization, demands efficient methods for managing the extensive domainspecific knowledge. While ontologies, structured sets of terms and relationships within a specific domain, have played a pivotal role in structuring agricultural data, existing models lack the precision and adaptability necessary for the dynamic agricultural environment. This paper proposes a novel framework that integrates the strengths of BERT (Bidirectional Encoder Representations from Transformers) GPT (Generative Pre-trained Transformer), and Graph Recurrent Q Learning Network (GRQLN) for developing a dynamic and efficient ontology tool. BERT-GPT, with its powerful natural language processing capabilities, allows for accurate feature extraction from diverse text data, including government and farming sources. Meanwhile, GRQLN leverages graph neural networks and reinforcement learning to convert these features into an ontology graph, optimizing the representation process. The integration of these advanced technologies not only addresses the limitations of existing models but also enhances the precision of ontological query retrieval by 8.5%, accuracy by 8.3%, recall by 10.4%, AUC by 9.5%, and specificity by 9.4%, while reducing delay by 4.5%. The proposed model is a significant contribution to the field, offering a robust tool that empowers stakeholders with actionable insights derived from a vast expanse of agricultural knowledge, ultimately facilitating informed decision-making in the ever-evolving landscape of agricultural operations.

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Management of Implicit Ontology Changes Generated by Non-conservative JSON Instance Updates in the τJOWL Environment

Brahmia,

Grandi

et al. 2024

Lecture Notes in Business Information Processing

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

Cited by 6 publications

References 47 publications

τSQWRL: A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data

τSQWRL: A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data

Enhancing Agricultural Knowledge Management using an efficient & Novel Ontology-Based Approach Leveraging BERT-GPT and Graph Recurrent Q Learning Network

Management of Implicit Ontology Changes Generated by Non-conservative JSON Instance Updates in the τJOWL Environment

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