DocumentCode
2336761
Title
A novel approach to semantic annotation based on multi-ontologies
Author
Wang, Peng ; Xu, Bao-wen ; Lu, Jian-jiang ; Kang, Da-Zhou ; Li, Yan-Wi
Author_Institution
Dept. of Comput. Sci. & Eng., Southeast Univ., Nanjing, China
Volume
3
fYear
2004
fDate
26-29 Aug. 2004
Firstpage
1452
Abstract
Semantic annotation is very crucial to the semantic Web. Most traditional researches just commit the Web resources to a single ontology. However, many practical semantic annotation cases need multi-ontologies. For the sake of solving the semantic annotation problem based on multi-ontologies, one prevalent approach is extending current ontology or integrating multi-ontologies, but both methods are complex problems which have no good solutions till now. Another approach used distributed description logic to denote multi-ontologies and the subsumption relations between concepts in different ontology with a simple bridge rule, but this approach could not describe more complex relations between multi-ontologies. In this paper, a novel approach is proposed to deal with the problem, and we employ a bridge ontology to express the complex relationships between multi-ontologies. The bridge ontology is a peculiar ontology, which can be created and maintained easily, but effective in semantic annotation applications based on multi-ontologies. The approach has the characteristics of low-cost, scalability, robust in the Web circumstance, avoiding the unnecessary ontology extending and integration, and promoting ontology reuse. A semantic annotation framework and an algorithm are proposed As well.
Keywords
ontologies (artificial intelligence); problem solving; semantic Web; bridge ontology; distributed description logic; multiontology integration; problem solving; semantic Web; semantic annotation framework; Bridges; Intelligent agent; Joining processes; Machine intelligence; Ontologies; Programmable logic arrays; Robustness; Semantic Web; Software quality; Web pages;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN
0-7803-8403-2
Type
conf
DOI
10.1109/ICMLC.2004.1382002
Filename
1382002
Link To Document