Multi-level semantic annotation and unified data integration using semantic web ontology in big data processing

Rani, Preeti; Suresh, R; Sethukarasi, R.

doi:10.1007/s10586-017-1029-7

Cited by 15 publications

(3 citation statements)

References 19 publications

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“…To deal with the issue of big data integration, several research works, like Refs. [16,19,20,24], have widely used ontologies not only since they allow to formalize the knowledge of any domain but also since they allow to have a unified view of big data, to extract reliable and consistent knowledge and facilitate reasoning and analytics on large amounts of data. Some other works, like Refs.…”

Section: Related Work Discussionmentioning

confidence: 99%

“…For such reason, it is useful to have appropriate tools that help constructing temporal ontologies for temporal and multi-version big data. However, by studying the state of the art of ontologies for big data [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] , we have noticed that there is no proposal for automatically creating a temporal ontology from temporal and multiversion big data. To fill this gap, we propose in this paper an approach, named JOWL (standing for Temporal OWL 2 from Temporal JSON), which allows: (i) to automatically build a temporal OWL 2 ontology of data, with the CWA, from temporal big data in JSON format, and (ii) to manage the incremental maintenance of this ontology in response to the evolution of the underlying time-varying JSON big data.…”

Section: Introductionmentioning

confidence: 99%

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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

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%

Section: Introductionmentioning

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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“…For fulfilling the inevitable need for Big Data applications especially data storage processing, various technologies have been allowed to handle Big Data (such as Hadoop and MapReduce), which offer more reliability, flexibility, scalability, and performance in a reasonable time and cost. However, the lack of interoperability between heterogeneous resources engenders an inherent issue, which makes data sharing and knowledge reuse a difficult task in Big Data applications (Rani et al , 2017). To overcome this challenge, it becomes imperative to endow Big Data with semantics and allow a standard view to ensuring efficient interoperability between applications (Chandrasekaran et al , 1999).…”

Section: Introductionmentioning

confidence: 99%

An intelligent system for energy management in smart cities based on big data and ontology

Sayah

Kazar

Lejdel³

et al. 2020

SASBE

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PurposeThis research paper aims at proposing a framework based on semantic integration in Big Data for saving energy in smart cities. The presented approach highlights the potential opportunities offered by Big Data and ontologies to reduce energy consumption in smart cities.Design/methodology/approachThis study provides an overview of semantics in Big Data and reviews various works that investigate energy saving in smart homes and cities. To reach this end, we propose an efficient architecture based on the cooperation between ontology, Big Data, and Multi-Agent Systems. Furthermore, the proposed approach shows the strength of these technologies to reduce energy consumption in smart cities.FindingsThrough this research, we seek to clarify and explain both the role of Multi-Agent System and ontology paradigms to improve systems interoperability. Indeed, it is useful to develop the proposed architecture based on Big Data. This study highlights the opportunities offered when they are combined together to provide a reliable system for saving energy in smart cities.Practical implicationsThe significant advancement of contemporary applications (smart cities, social networks, health care, IoT, etc.) requires a vast emergence of Big Data and semantics technologies in these fields. The obtained results provide an improved vision of energy-saving and environmental protection while keeping the inhabitants’ comfort.Originality/valueThis work is an efficient contribution that provides more comprehensive solutions to ontology integration in the Big Data environment. We have used all available data to reduce energy consumption, promote the change of inhabitant’s behavior, offer the required comfort, and implement an effective long-term energy policy in a smart and sustainable environment.

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A Document Visualization Strategy Based on Semantic Multimedia Big Data

Rinaldi

2019

Pervasive Systems, Algorithms and Networks

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Multi-level semantic annotation and unified data integration using semantic web ontology in big data processing

Cited by 15 publications

References 19 publications

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

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

An intelligent system for energy management in smart cities based on big data and ontology

A Document Visualization Strategy Based on Semantic Multimedia Big Data

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