DocumentCode :
1895181
Title :
Research on Ontology Integration Combined with Machine Learning
Author :
Zhu, Li ; Yang, Qing ; Chen, Wei
Author_Institution :
Dept. of Comput. Sci., Huazhong Normal Univ., Wuhan, China
Volume :
1
fYear :
2009
fDate :
10-11 Oct. 2009
Firstpage :
464
Lastpage :
467
Abstract :
Recently ontologies are playing very important part in many areas, such as intelligent information retrieve, knowledge management and organization, electronic commerce and so on, however, several drawbacks must be overcome before ontologies become useful and practical tools. As the number of ontologies are made publicly available and accessible on the Web increases steadily, a single ontology is no longer enough to support the tasks envisaged by a distributed environment like the semantic Web. Multiple ontologies need to be accessed for several applications. A critical issue is ontology integration, which can largely improve the efficiency to enrich such a domain ontology with less time and lower cost for obtaining related knowledge. This paper has deeply studied the principles of ontology integration, then proposes a procedure model for ontology construction and a new framework for ontology integration based on machine learning through analyzing the characteristics and problems in the process of ontology integration.
Keywords :
learning (artificial intelligence); ontologies (artificial intelligence); semantic Web; machine learning; ontology construction; ontology integration; semantic Web; Automation; Computer science; Costs; Electronic commerce; Information retrieval; Knowledge management; Learning systems; Machine learning; Ontologies; Semantic Web; machine learning; ontology integration; semantic matching; semantic web;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
Conference_Location :
Changsha, Hunan
Print_ISBN :
978-0-7695-3804-4
Type :
conf
DOI :
10.1109/ICICTA.2009.119
Filename :
5287613
Link To Document :
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