On Storing Ontologies including Fuzzy Datatypes in Relational Databases

Campaña, Jesús R.; Medina, Juan Miguel; Pons, Olga

doi:10.1109/fuzzy.2007.4295624

Cited by 17 publications

(12 citation statements)

References 7 publications

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“…Definition 1: A fuzzy ontology of Traditional Chinese Medicine is a five-tuple, FO={C, I, A, R, T} [5], in which C is the set of classes, I is the set of instances, A is the set of attributes, R is the set of relations and T is the set of truths.…”

Section: Representation Methods Of Fuzzy Ontologymentioning

confidence: 99%

“…Interval: [param1,param2,param3,param4], in which, param1 represents the maximum value of the possibility of 0, param2 and param3 denote the minimum value and the maximum value of the possibility of 1, param4 indicates the minimum value of the possibility of 0. For example, '半夏在气温 10℃左右时萌发出苗', '10℃左右' is a fuzzy data, which can be represented as Interval:[5,10,10,15].…”

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Research on storage method based on fuzzy ontology

Sun

Wang

2013

2013 International Conference on Computer Communication and Informatics

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In many fields the traditional ontology cannot represent and handle the inaccurate and uncertain information existing in the real world. The usual solution is introducing fuzzy logic into ontology to deal with such fuzzy information [1]. Currently, a large number of studies have been devoted to the construction of fuzzy ontology and made some achievements [2]. However, it is necessary to choose a suitable method to store the fuzzy ontology when it is applied to an actual system. Compared with the classical storage methods of ontology, storing fuzzy ontology not only meets the requirements of the classical ontology storage schema, but also handles a large number of fuzzy data, which brings great challenge to the storage of fuzzy ontology [3]. This paper proposes a new mixed storage mode based on relation database under the background of Traditional Chinese Medicine (TCM).

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Section: Representation Methods Of Fuzzy Ontologymentioning

confidence: 99%

mentioning

confidence: 99%

Research on storage method based on fuzzy ontology

Sun

Wang

2013

2013 International Conference on Computer Communication and Informatics

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“…With these considerations in mind, we propose a RDBMS hybrid schema to store ontologies and their instances, allowing a separate or combined use. A summarized version can be seen in [9].…”

Section: Owl Ontology Storage In Ordbmsmentioning

confidence: 99%

Semantic Data Management Using Fuzzy Relational Databases

Campaña

Medina

Vila

2013

Flexible Approaches in Data, Information and Knowledge Management

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This chapter presents a schema and a transformation algorithm to store OWL ontologies in Object Relational Databases. The database schema allows the storage of an ontology structure, while the transformation algorithm creates an appropriate schema to store its instances preserving all information. We allow the use of instance data of imprecise nature, mostly fuzzy numerical data. An OWL ontology is defined allowing numerical fuzzy datatypes as the range of properties. In order to manage all the information, instance data handling is delegated onto a Fuzzy ORDBMS, which is briefly described. We present here a complete description of the structures conforming the storage schema proposed, and the algorithms used to transform the OWL ontology to a database schema. We also discuss the role of ontologies as relational database design tools.

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“…The authors in [22] propose a database schema to store ontology along with its instances preserving all information. Ontology and instances are stored in different schemas in order to improve the access to instances while retaining the capability of reasoning over the ontology.…”

Section: Transformation Of Fuzzy Ontologies To Databasesmentioning

confidence: 99%

A Framework for Fuzzy Ontology Storing onto Relational Database within an a Priori Data Integration System

Berber¹,

Beldjilali²,

Lamolle³

2015

IJCA

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Resolving semantic heterogeneity is still a challenging issue in data integration systems; but it can be strongly fixed when using ontology in an a priori approach where local ontology concepts are linked with shared ontology prior to populating data in corresponding sources. In this paper, we describe a defying context where local source is described by a fuzzy OWL ontology within an integration system using an a priori approach to achieve automatic integration for new data sources. We propose a conceptual framework starting by shared ontology and producing a target fuzzy Relational Database for every ontology-based local source participating in the integration system. Assuming shared ontology is a consensus in a given domain, this framework provides various contributions. It aims to solve ahead the problem of heterogeneous data sources because the local ontology that references the shared ontology is used to generate the conceptual data model for the target fuzzy Relational Database. To do this, it extends the a priori approach to deal with uncertainty which is a very common requirement in real world applications. Its storage process may be run on most of popular RDBMS. It is using a fuzzy OWL which represents most of fuzzy ontology constructs.

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On Storing Ontologies including Fuzzy Datatypes in Relational Databases

Cited by 17 publications

References 7 publications

Research on storage method based on fuzzy ontology

Research on storage method based on fuzzy ontology

Semantic Data Management Using Fuzzy Relational Databases

A Framework for Fuzzy Ontology Storing onto Relational Database within an a Priori Data Integration System

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