An important precondition for the success of the Semantic Web is founded
on the principle that the content of web pages will be semantically
annotated. This paper proposes a method of automatically acquiring
semantic annotations (AASA). In the AASA method, we employ a combination
of data mining and optimization to acquire semantic annotations.
Key features of AASA include combining association rules, inference
mechanism, genetic algorithm and self-organizing map to create semantic
annotations, and using the k-nearest-neighbor query combined with
simulated annealing to maintain semantic annotations.
%0 Journal Article
%1 LixinHan08012007
%A Han, Lixin
%A Chen, Guihai
%A Xie, Li
%D 2007
%J Journal of Information Science
%K toread
%N 4
%P 435-450
%R 10.1177/0165551506072164
%T AASA: a Method of Automatically Acquiring Semantic Annotations
%U http://jis.sagepub.com/cgi/content/abstract/33/4/435
%V 33
%X An important precondition for the success of the Semantic Web is founded
on the principle that the content of web pages will be semantically
annotated. This paper proposes a method of automatically acquiring
semantic annotations (AASA). In the AASA method, we employ a combination
of data mining and optimization to acquire semantic annotations.
Key features of AASA include combining association rules, inference
mechanism, genetic algorithm and self-organizing map to create semantic
annotations, and using the k-nearest-neighbor query combined with
simulated annealing to maintain semantic annotations.
@article{LixinHan08012007,
abstract = {An important precondition for the success of the Semantic Web is founded
on the principle that the content of web pages will be semantically
annotated. This paper proposes a method of automatically acquiring
semantic annotations (AASA). In the AASA method, we employ a combination
of data mining and optimization to acquire semantic annotations.
Key features of AASA include combining association rules, inference
mechanism, genetic algorithm and self-organizing map to create semantic
annotations, and using the k-nearest-neighbor query combined with
simulated annealing to maintain semantic annotations.},
added-at = {2008-04-07T10:14:08.000+0200},
author = {Han, Lixin and Chen, Guihai and Xie, Li},
biburl = {https://www.bibsonomy.org/bibtex/21e72917a7fb80e551ee73d0af849fe0d/enterldestodes},
doi = {10.1177/0165551506072164},
eprint = {http://jis.sagepub.com/cgi/reprint/33/4/435.pdf},
interhash = {fd82ba478ef7cf990f5cc511869a0084},
intrahash = {1e72917a7fb80e551ee73d0af849fe0d},
journal = {Journal of Information Science},
keywords = {toread},
note = {toread},
number = 4,
owner = {reinhard},
pages = {435-450},
review = {Still to read; Title and abstract promising},
timestamp = {2008-04-07T10:16:20.000+0200},
title = {{AASA: a Method of Automatically Acquiring Semantic Annotations}},
url = {http://jis.sagepub.com/cgi/content/abstract/33/4/435},
volume = 33,
year = 2007
}