The Semantic Web relies heavily on formal ontologies to structure
data for comprehensive and transportable machine understanding. Thus,
the proliferation of ontologies factors largely in the Semantic Web's
success. The authors present an ontology learning framework that
extends typical ontology engineering environments by using semiautomatic
ontology construction tools. The framework encompasses ontology import,
extraction, pruning, refinement and evaluation.
%0 Journal Article
%1 Maedche2001
%A Maedche, A.
%A Staab, S.
%D 2001
%J Intelligent Systems, IEEE
%K (artificial Internet; Semantic Web; XML acquisition; construction data; documents; engineering environments; hypermedia information intelligence); knowledge languages; learning learning; machine markup meta metadata; ontology resources; semiautomatic tools; understanding;
%N 2
%P 72 - 79
%R 10.1109/5254.920602
%T Ontology learning for the Semantic Web
%V 16
%X The Semantic Web relies heavily on formal ontologies to structure
data for comprehensive and transportable machine understanding. Thus,
the proliferation of ontologies factors largely in the Semantic Web's
success. The authors present an ontology learning framework that
extends typical ontology engineering environments by using semiautomatic
ontology construction tools. The framework encompasses ontology import,
extraction, pruning, refinement and evaluation.
@article{Maedche2001,
abstract = { The Semantic Web relies heavily on formal ontologies to structure
data for comprehensive and transportable machine understanding. Thus,
the proliferation of ontologies factors largely in the Semantic Web's
success. The authors present an ontology learning framework that
extends typical ontology engineering environments by using semiautomatic
ontology construction tools. The framework encompasses ontology import,
extraction, pruning, refinement and evaluation.},
added-at = {2013-01-31T09:21:26.000+0100},
author = {Maedche, A. and Staab, S.},
biburl = {https://www.bibsonomy.org/bibtex/230448ea5849898a5fc43d5ea6722a4ac/kkrieger},
doi = {10.1109/5254.920602},
interhash = {d0798c282eab793a48ef70ce0a5572a8},
intrahash = {30448ea5849898a5fc43d5ea6722a4ac},
issn = {1541-1672},
journal = {Intelligent Systems, IEEE},
keywords = {(artificial Internet; Semantic Web; XML acquisition; construction data; documents; engineering environments; hypermedia information intelligence); knowledge languages; learning learning; machine markup meta metadata; ontology resources; semiautomatic tools; understanding;},
number = 2,
owner = {kkrieger},
pages = { 72 - 79},
timestamp = {2013-01-31T09:21:31.000+0100},
title = {Ontology learning for the Semantic Web},
volume = 16,
year = 2001
}