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