Rules Driven Object-Relational Databases Ontology Learning

Author

Chen, Jia ; Wu, Yue ; Li, Ming ; Li, Shuquan ; You, Jing

Author_Institution

Comput. Sci. Dept., Univ. of Electron. Sci. & Technol. of China, Chengdu, China

fYear

2009

fDate

21-22 April 2009

Firstpage

169

Lastpage

174

Abstract

A rules driven ontology learning approach from object-relational databases (ORDB) is proposed in this paper. The method extracts ontology from the Object-Relational Databases based on the Semantic Objects (SO) platform. And the core of this method aims at proposing a set of rules to obtain the basic elements which Web Ontology Language (OWL) needs. Based on the rules, the RDOL model is proposed. The model is driven by the rules to generate ontology. Our proposed approach reduces about forty-two percent learning time and obtains about eighteen percent higher performance comparing to the ontology learning from Relational databases (RDB).

Keywords

knowledge representation languages; learning (artificial intelligence); ontologies (artificial intelligence); relational databases; RDOL model; Web Ontology Language; object-relational databases; rules driven ontology learning; semantic objects; Application software; Communication system software; Data models; Machine learning; OWL; Object oriented databases; Object oriented modeling; Ontologies; Relational databases; Spatial databases; OWL; Object-Relational Databases; Ontology; Ontology Learning; Rules Driven; Semantic Objects;

fLanguage

English

Publisher

ieee

Conference_Titel

Interoperability for Enterprise Software and Applications China, 2009. IESA '09. International Conference on

Conference_Location

Beijing

Print_ISBN

978-0-7695-3652-1

Type

conf

DOI

10.1109/I-ESA.2009.33

Filename

5260837