An Approach For Transforming of Relational Databases to OWL Ontology

Dadjoo, Mona; Kheirkhah, Esmaeil

doi:10.5121/ijwest.2015.6102

Cited by 31 publications

(12 citation statements)

References 21 publications

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“…We also recovered a super table through the proposed method for rebuilding hierarchies from specialization inheritance for the 'tables "film" and "film_text". We used the same transformation rules used in [6] to obtain the result ontology ( Fig. 2).…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In [6], the authors proposed an approach for automatic ontology construction from relational databases. This approach uses as input a relational database written in SQL and its output is OWL ontology.…”

Section: Related Workmentioning

confidence: 99%

“…The relational model is the most popular model due to its simplicity and ability to process data with storage efficiency, it also contain hidden semantics that can be exploited during the ontology extraction. The relational model lacks expressiveness compared to the conceptual model, that is why some existing approaches [3]- [6] offered to do a reverse engineering to transform the relational model to a conceptual model because the latter is semantically richer.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using Reverse Engineering for Building Ontologies with Deeper Taxonomies from Relational Databases

Sbai¹,

Louhdi²,

Behja³

et al. 2019

JSW

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The relational model is characterized by its high quality and has been widely used by information systems. However, unlike the conceptual model, the relational model is semantically poor since it doesn't enable the representation of inheritance. In this paper, we present an algorithmic approach to extract generalization/ specialization inheritance hierarchies. We perform a reverse engineering by analyzing stored data. Finally, we evaluated our approach by conducting several experiments on relational databases. The results were satisfying in terms of recovering the tables lost during the transformation from the entity relationship model to the relational model.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Reverse Engineering for Building Ontologies with Deeper Taxonomies from Relational Databases

Sbai¹,

Louhdi²,

Behja³

et al. 2019

JSW

View full text Add to dashboard Cite

show abstract

“…Telnarova [19] has expressed that the relational database is the source of ontology creation and presented an approach for creating ontology using relational databases to ontology mapping. Mona et al [20] have proposed an approach for constructing ontologies from relational databases. In this approach, the ontological concepts have been generated by extracting knowledge from the relational schema and database components such as constraints and triggers.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatically Converting Tabular Data to RDF: an Ontological Approach

Sharma¹,

Marjit²,

Biswas³

2015

IJWesT

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“…Mona Dadjoo divides the mapping method between the ontology and the database into three types: method based on logic model, method based on concept model and intermediate model method based on concept; furthermore, he puts forward an automatic mapping method, which also takes the trigger into account [3].…”

Section: Introductionmentioning

confidence: 99%

Relational Database Ontology Discovery Method Based on Formal Concept Analysis

Gao¹,

Liang²,

Qiao³

2017

Proceedings of the 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016)

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Abstract. This paper elaborates how to use the formal concept analysis method to map contents in the database to the ontology, in order to provide the big data application with high-quality data source by virtue of integrating the database with Semantic Web. In recent times, a mass of data is stored in the relational database, but such data with low share usage fails to play its full role. On account that the big data application has grown by leaps and bounds, a large number of shared data is urgently needed. By mapping the data in the relational database into the ontology, the technology of Semantic Web can provide a lot of semantic data to the big data application, which is conducive to big data analysis and use. In this paper, the ontology is built by taking the formal concept as the intermediate model and converting the logic structure of database into Hasse graph and context table, and then combining with the domain knowledge. The ontology in the knowledge domain can be found from the database by applying the formal concept analysis method, which takes full advantage of logical structure information of the database and is beneficial for automation found by the ontology. Eventually, ontology method and problems found in the relational database by virtue of the formal concept analysis are summarized herein..

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An Approach For Transforming of Relational Databases to OWL Ontology

Abstract: ABSTRACT

Cited by 31 publications

References 21 publications

Using Reverse Engineering for Building Ontologies with Deeper Taxonomies from Relational Databases

Using Reverse Engineering for Building Ontologies with Deeper Taxonomies from Relational Databases

Automatically Converting Tabular Data to RDF: an Ontological Approach

Relational Database Ontology Discovery Method Based on Formal Concept Analysis

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