Ontology Transformation to Conceptual Data Model U

Trinkunas, Justas; Vasilecas, Olegas

doi:10.1007/978-0-387-78578-3_38

Cited by 16 publications

(17 citation statements)

References 7 publications

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“…A method for transformation of existing knowledge sources into conceptual data models is proposed in [10]. Although it focuses on rather opposite direction of the transformation and shows an example of transformation of an OWL ontology into a Power Designer conceptual model, it also mentions Freebase as a possible resource.…”

Section: Related Researchmentioning

confidence: 99%

Exploiting Freebase to Obtain GoodRelations-Based Product Ontologies

Dudáš

Zamazal

Mynarz

et al. 2014

Lecture Notes in Business Information Processing

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Abstract. Application of semantic web technologies in e-commerce depends on the availability of product ontologies. However, such ontologies are not yet available for many industries and developing them from scratch is costly. We present a method of reusing parts of the Freebase schema by transforming it into a GoodRelations-based product ontology, using our Pattern-based Ontology Transformation Framework. We demonstrate our method on a part of the Freebase Medicine schema, which we transformed into a product ontology covering prescription drugs.

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Section: Related Researchmentioning

confidence: 99%

Exploiting Freebase to Obtain GoodRelations-Based Product Ontologies

Dudáš

Zamazal

Mynarz

et al. 2014

Lecture Notes in Business Information Processing

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“…In [42] an automatic transformation from ontology to the conceptual ER data model is presented. The graph oriented approach used, converts an ontology expressed in OWL DL into an ER model.…”

Section: Ontologies As a Tool For Relational Database Designmentioning

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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“…This framework is not included meta-modeling about schemaless databases. In [26], a graph method has been used to transfer ontology specification in OWL language towards ER (Entity-Relation) diagram of relational data bases. Yet, both of these methodologies are not capable to provide rich formal semantics for universal notion of heterogeneous databases.…”

Section: Related Workmentioning

confidence: 99%