User-Driven Ontology Learning from Structured Data

Author

Jacinto, Carlos ; Antunes, Cláudia

Author_Institution

Dept. of Comput. Sci. & Eng., Inst. Super. Tecnico, Lisbon, Portugal

fYear

2012

fDate

May 30 2012-June 1 2012

Firstpage

184

Lastpage

189

Abstract

The automatic acquisition of models to represent existing domain knowledge is a key step to further develop domain driven data mining. Ontology Learning has been mostly focused on unstructured data sources, as text, leaving structured data almost ignored. This is probably due to the existence of a model behind that kind of data, that without being an ontology, reveals some data semantics. This paper extends the work by Borgida [1], giving to the user the possibility to choose the level of detail of a domain ontology learnt from a relational database. Beside the full exploration of relational model premises, we apply association rules mining to discover basic axioms, which describe the hidden assertions underlying the domain.

Keywords

data mining; data structures; learning (artificial intelligence); ontologies (artificial intelligence); relational databases; association rules mining; automatic model acquisition; domain driven data mining; knowledge discovery techniques; relational database; relational model; structured data; unstructured data sources; user-driven ontology learning; Association rules; Data models; Motion pictures; Ontologies; Relational databases; Ontology learning; Pattern mining; Relational databases;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Information Science (ICIS), 2012 IEEE/ACIS 11th International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4673-1536-4

Type

conf

DOI

10.1109/ICIS.2012.115

Filename

6211095