An optimized ontology transfer learning method

Author

Tian, Hong ; Li, Yunhong ; Liu, Hongbo ; Abraham, Ajith

Author_Institution

Sch. of Software, Dalian Jiaotong Univ., Dalian, China

fYear

2010

fDate

8-10 Oct. 2010

Firstpage

569

Lastpage

572

Abstract

Recently, more and more research is devoted for ontology in the semantic web domain. Firstly, a method for choosing the set of candidate similar concepts is presented based on ontology graphical structural features and data mining. Secondly, a calculation method of conceptual similarity is proposed based on the characteristics of the concept ontology and information content. Finally, the optimized ontology can be transferred into learning. Experimental results illustrate that this method is effective for computing the concept similarity and ontology can be transferred successfully to learn.

Keywords

data mining; learning (artificial intelligence); ontologies (artificial intelligence); semantic Web; concept ontology; conceptual similarity method; data mining; ontology graphical structural features; ontology transfer learning method; semantic Web; Complexity theory; Computers; Government; Lattices; Ontologies; Semantic Web; Semantics; concept similarity; data mining; ontology; transfer learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Information Systems and Industrial Management Applications (CISIM), 2010 International Conference on

Conference_Location

Krackow

Print_ISBN

978-1-4244-7817-0

Type

conf

DOI

10.1109/CISIM.2010.5643515

Filename

5643515