DocumentCode :
425861
Title :
Resource association discovery in semantic Web
Author :
Bangyong, Liang ; Jie, Tang ; Juanzi, Li ; Kehong, Wang
Author_Institution :
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing
fYear :
2004
fDate :
15-15 Sept. 2004
Firstpage :
266
Lastpage :
269
Abstract :
Resources on the Web have direct and hidden relations. Direct relations mean the visible link between two resources like hyperlinks. Direct relations are easy to discover while hidden relations are not. Current discovery methods are mostly based on the text learning and user feedback methods. The text contents are not fit for inference and they are also lack of semantics. The Web pages are annotated with ontologies in semantic Web. The annotations are useful for inference. We propose a framework for resource association discovery in semantic Web. The framework uses the annotation for inference and the annotation hierarchy can be solved by mapping different ontologies. The experiment shows the satisfied results. Finally, the conclusion and future work are discussed
Keywords :
data mining; hypermedia; ontologies (artificial intelligence); semantic Web; text analysis; annotation hierarchy; hyperlinks; inference annotation; resource association discovery; semantic Web ontology; text learning method; user feedback method; Collaboration; Computer science; Feedback; Keyboards; Knowledge engineering; Mice; Ontologies; Relational databases; Semantic Web; Web pages;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
E-Commerce Technology for Dynamic E-Business, 2004. IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7695-2206-8
Type :
conf
DOI :
10.1109/CEC-EAST.2004.56
Filename :
1388334
Link To Document :
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