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Fwd: One use case for the ontology-lexicon model

From: Philipp Cimiano <cimiano@cit-ec.uni-bielefeld.de>
Date: Thu, 26 Apr 2012 20:39:43 +0200
Message-ID: <4F99966F.2000300@cit-ec.uni-bielefeld.de>
To: "public-ontolex@w3.org" <public-ontolex@w3.org>
Dear all,

  our first use case description. We will discuss this during our next 
telco.

Best regards,

Philipp.

-------- Original-Nachricht --------
Betreff: 	One use case for the ontology-lexicon model
Datum: 	Thu, 26 Apr 2012 11:35:29 +0200
Von: 	Ondrej Zamazal <ondrej.zamazal@vse.cz>
An: 	cimiano@cit-ec.uni-bielefeld.de
CC: 	Ondrej Zamazal <ondrej.zamazal@vse.cz>, svatek@vse.cz



Dear Philipp Cimiano,
we send you our use case (please see filled template below) for the ontology-lexicon model which is in the context of ontology transformation.

Regards,
Ondrej and Vojtech

<TEMPLATE>

Ontology transformation enhanced by lexical information
owner: Ondřej Zamazal (ondrej.zamazal@vse.cz), Vojtěch Svátek (svatek@vse.cz)

I. Motivation

With the help of lexical information, different applications can better
work with alternative modelling styles employed in an ontology. Ontology
transformation [1] in this context means modification of an ontology in
terms of its structural and naming aspects. Lexical information would help
detect ontology fragments to be transformed and generate the new variants
of those ontology fragments.

II. Description of the use case

Ontology transformation basically consists of three steps: detection of
ontology fragments to be transformed, generation of transformation
instructions, and transformation as such. Ontology fragments to be
transformed are detected using the structural aspect (employed axioms) and
naming aspect (names of entities). Regarding the names of entities (local
fragment of an IRI), they are usually very short and their analysis can
hardly reveal much of the underlying lexical background. Using labels can
only slightly improve the situation. Therefore, explicit representation of
lexical characteristics of entity names could contribute to resolution of
this 'detection bottleneck' and consequently increase the quality of the
transformation result, see example.

III. Limitations of existing models

Currently, NLP-based techniques applied on local IRI fragments or labels of
entities often label fail due to lack of material for proper analysis and
conclusion.

IV. Example

Let us consider that we want to change (here, unfold) the representation of
a concept by a named entity to its alternative modelling style, i.e. class
A will be replaced by the definition 'p some B'.
Particularly, we would like to transform the 'AcceptedPaper' named entity
into 'Paper and (hasDecision some Acceptance)'.
In order to properly perform such transformation, we would need to get the
following lexical information:
* main term of AcceptedPaper
* noun form of modal adjective part of noun phrase, i.e. Accepted ->
acceptance
* hint about the naming property p (i.e. hasDecision), e.g. reference to a
related activity.

V. Requirements

References
[1] O. Šváb-Zamazal, V. Svátek, and L. Iannone. Pattern-based ontology
transformation service exploiting OPPL and OWL-API. In Knowledge
Engineering and Knowledge Management by the Masses. EKAW-2010., 2010.

<TEMPLATE>

-- 
Ondrej Svab-Zamazal, Ph.D.
University of Economics, Prague
Faculty of Informatics and Statistics
Department of Information and Knowledge Engineering
ondrej.zamazal@vse.cz	http://nb.vse.cz/~svabo
Received on Thursday, 26 April 2012 18:40:15 GMT

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