Ontology Automatic Constructing Based on Relational Database

Liu, Peng; Wang, Xiaoying; Bao, Aihua; Wang, Xiaoxuan

doi:10.1109/gcc.2010.86

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…At present, methods of extracting the ontology from relational database are: 1) rule-based ontology extraction method [2,3,4,5,6]; this method extracts the ontology by defining a series of rules. The method is simple and reliable, but has a poor versatility.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Ontology Extraction Oriented to Non-Normalized Database

Cui

Wang

2013

AMR

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Ontology has be applied in many fields, such as data integration, system interoperability, etc. At present, there are many ontology extraction methods based on relational database, but they all assume that the database schema is at least in third normal form (3NF). A set of improved rules to extract ontology is presented in this paper. The ontology generated is described with the OWL language. Compared with other existing methods, this approach can be able to identify a variety of strategies about non-normalized design of relational databases and deal with them, while none of existing methods can identify all of them. The results show that our method provides a more accurate process of ontology learning from non-normalized relational database.

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Section: Introductionmentioning

confidence: 99%

Implementation of Ontology Extraction Oriented to Non-Normalized Database

Cui

Wang

2013

AMR

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Ontology learning from relational databases

Lakzaei

Shamsfard

2021

Information Sciences

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Automatic generation of ontology from data models: A practical evaluation of existing approaches

Idrissi

Baïna

Bäına

2013

IEEE 7th International Conference on Research Challenges in Information Science (RCIS)

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Nowadays, ontology as a knowledge sharing approach plays an important role in semantic interoperability of enterprise applications (EAs). However, the manual process of ontology construction requires deep understanding of the domain. This approach is difficult, expensive and time-consuming. To overcome the knowledge acquisition bottleneck, ontology learning field aims to provide automatic and semi-automatic approaches for ontology generation. Several approaches have been emerged for this purpose. In this paper, we present a practical study of methods that take data models as input to the learning process. The main contributions of this work are: (i) the evaluation of the availability of existing approaches for (semi-)automatic generation of ontology from data models; (ii) the evaluation of tools according to their operability; and (iii) the evaluation of the resulting ontologies to assess their quality in supporting semantic interoperability. Our goal through this study is to find a response to the question: Is there a tool that extracts (semi-)automatically an application ontology from data models, intended for use in semantic interoperability?.

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Ontology Automatic Constructing Based on Relational Database

Cited by 5 publications

References 3 publications

Implementation of Ontology Extraction Oriented to Non-Normalized Database

Implementation of Ontology Extraction Oriented to Non-Normalized Database

Ontology learning from relational databases

Automatic generation of ontology from data models: A practical evaluation of existing approaches

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