Ontology Learning and Population: Bridging the Gap between Text and
Knowledge
P. Buitelaar, and P. Cimiano (Eds.) Frontiers in Artificial Intelligence and Applications IOS Press, Amsterdam, (2008)
Abstract
The promise of the Semantic Web is that future web pages will be annotated
not only with bright colors and fancy fonts as they are now, but
with annotation extracted from large domain ontologies that specify,
to a computer in a way that it can exploit, what information is contained
on the given web page. The presence of this information will allow
software agents to examine pages and to make decisions about content
as humans are able to do now. The classic method of building an ontology
is to gather a committee of experts in the domain to be modeled by
the ontology, and to have this committee agree on which concepts
cover the domain, on which terms describe which concepts, on what
relations exist between each concept and what the possible attributes
of each concept are. All ontology learning systems begin with an
ontology structure, which may just be an empty logical structure,
and a collection of texts in the domain to be modeled. An ontology
learning system can be seen as an interplay between three things:
an existing ontology, a collection of texts, and lexical syntactic
patterns. The Semantic Web will only be a reality if we can create
structured, unambiguous ontologies that model domain knowledge that
computers can handle. The creation of vast arrays of such ontologies,
to be used to mark-up web pages for the Semantic Web, can only be
accomplished by computer tools that can extract and build large parts
of these ontologies automatically. This book provides the state-of-art
of many automatic extraction and modeling techniques for ontology
building. The maturation of these techniques will lead to the creation
of the Semantic Web.
%0 Book
%1 BuitelaarCimiano2008
%B Frontiers in Artificial Intelligence and Applications
%C Amsterdam
%D 2008
%E Buitelaar, Paul
%E Cimiano, Philipp
%I IOS Press
%K gap ontology_learning nlp2rdf_relevant
%T Ontology Learning and Population: Bridging the Gap between Text and
Knowledge
%U http://wtlab.um.ac.ir/parameters/wtlab/filemanager/resources/Ontology%20Learning/ONTOLOGY%20LEARNING%20AND%20POPULATION%20BRIDGING%20THE%20GAP%20BETWEEN%20TEXT%20AND%20KNOWLEDGE.pdf
%V 167
%X The promise of the Semantic Web is that future web pages will be annotated
not only with bright colors and fancy fonts as they are now, but
with annotation extracted from large domain ontologies that specify,
to a computer in a way that it can exploit, what information is contained
on the given web page. The presence of this information will allow
software agents to examine pages and to make decisions about content
as humans are able to do now. The classic method of building an ontology
is to gather a committee of experts in the domain to be modeled by
the ontology, and to have this committee agree on which concepts
cover the domain, on which terms describe which concepts, on what
relations exist between each concept and what the possible attributes
of each concept are. All ontology learning systems begin with an
ontology structure, which may just be an empty logical structure,
and a collection of texts in the domain to be modeled. An ontology
learning system can be seen as an interplay between three things:
an existing ontology, a collection of texts, and lexical syntactic
patterns. The Semantic Web will only be a reality if we can create
structured, unambiguous ontologies that model domain knowledge that
computers can handle. The creation of vast arrays of such ontologies,
to be used to mark-up web pages for the Semantic Web, can only be
accomplished by computer tools that can extract and build large parts
of these ontologies automatically. This book provides the state-of-art
of many automatic extraction and modeling techniques for ontology
building. The maturation of these techniques will lead to the creation
of the Semantic Web.
%@ 978-1-58603-818-2
@book{BuitelaarCimiano2008,
abstract = {The promise of the Semantic Web is that future web pages will be annotated
not only with bright colors and fancy fonts as they are now, but
with annotation extracted from large domain ontologies that specify,
to a computer in a way that it can exploit, what information is contained
on the given web page. The presence of this information will allow
software agents to examine pages and to make decisions about content
as humans are able to do now. The classic method of building an ontology
is to gather a committee of experts in the domain to be modeled by
the ontology, and to have this committee agree on which concepts
cover the domain, on which terms describe which concepts, on what
relations exist between each concept and what the possible attributes
of each concept are. All ontology learning systems begin with an
ontology structure, which may just be an empty logical structure,
and a collection of texts in the domain to be modeled. An ontology
learning system can be seen as an interplay between three things:
an existing ontology, a collection of texts, and lexical syntactic
patterns. The Semantic Web will only be a reality if we can create
structured, unambiguous ontologies that model domain knowledge that
computers can handle. The creation of vast arrays of such ontologies,
to be used to mark-up web pages for the Semantic Web, can only be
accomplished by computer tools that can extract and build large parts
of these ontologies automatically. This book provides the state-of-art
of many automatic extraction and modeling techniques for ontology
building. The maturation of these techniques will lead to the creation
of the Semantic Web.},
added-at = {2010-01-12T11:48:39.000+0100},
address = {Amsterdam},
biburl = {https://www.bibsonomy.org/bibtex/26c49dae9157532a01415c35abc7091d1/sebastian},
description = {Entire set of citations (10/2009)},
editor = {Buitelaar, Paul and Cimiano, Philipp},
file = {IOS Product page:http\://www.iospress.nl/html/9781586038182:URL},
interhash = {985072a36f1f789052de6678e17e4eb8},
intrahash = {6c49dae9157532a01415c35abc7091d1},
isbn = {978-1-58603-818-2},
keywords = {gap ontology_learning nlp2rdf_relevant},
owner = {flint},
publisher = {IOS Press},
series = {Frontiers in Artificial Intelligence and Applications},
timestamp = {2013-07-07T16:28:17.000+0200},
title = {Ontology Learning and Population: Bridging the Gap between Text and
Knowledge},
url = {http://wtlab.um.ac.ir/parameters/wtlab/filemanager/resources/Ontology%20Learning/ONTOLOGY%20LEARNING%20AND%20POPULATION%20BRIDGING%20THE%20GAP%20BETWEEN%20TEXT%20AND%20KNOWLEDGE.pdf},
volume = 167,
year = 2008
}