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tology is already designed (on a paper for example),
and therefore it remains only to edit it in order to make
it interpretable by computers. The second group con-
sists of tools that aim to design ontologies in a su-
pervised work (semi-automatic) according to one or
more design steps. Next sections discuss some meth-
ods and tools for building ontologies.
2.1.1 Ontologies Editors
In the domain of ontologies design, there exist sev-
eral design tools. This section presents some of these
tools.
Prot
´
eg
´
e. Prot´eg´e (Knublauch et al., 2004) is a plat-
form for developing OWL ontologies. Its architec-
ture, based on plug-ins, allows a designer to add new
features like intelligent reasoning, test, maintenance,
etc. Originally designed on a frame-based model,
nowadays the most used version is Prot´eg´e-OWL.
This version consists of a set of plug-ins developed
above the Prot´eg´e kernel and dedicated for ontology
construction according to the OWL ontology model.
PLIBEditor. PLIBEditor supports the creation of
ontologies conforming to the PLIB ontology model
(Pierra, 2003). This editor has the advantage of per-
sisting ontologies objects in a database designed ac-
cording to a database architecture called ontology-
based database (Dehainsala et al., 2007). This
database stores ontologies model, ontologiesand their
instances.
DOE. Unlike Prot´eg´e and PLIBEditor that put a
strong emphasis on the formal representation of con-
cepts of an ontology, the DOE editor (Troncy et al.,
2003) suggests to structure the informal description
of concepts to describe more precisely these concepts.
This editor uses a specific semantics called ”differen-
tial semantics” to document the generalization / spe-
cialization hierarchies by applying four basic rules
(Bachimont et al., 2002): (1) similarity with parent,
(2) difference with parent, (3) similarity with siblings
and (4) difference with siblings.
In addition to the tool features, annotation techniques
allow the designer to keep for example, relationship
between ontologies and annotated documents. But
another way to maintain this relationship consists in
using texts to build ontologies.
2.1.2 Tools for Supervised Design: Designing
Ontologies from Texts
Designing ontologies, on the basis of its consensual
nature, is a very difficult task. Some tools however,
propose building approaches to lighten this problem.
Some of these tools provide an ontology construction
starting from texts.
Text2Onto. Developed at the University of Karl-
sruhe, Text2Onto is a tool that implements text min-
ing algorithms on textual data for building ontologies
semi-automatically (Cimiano and Volker, 2005). It in-
cludes several data processing: terms extraction using
either statistical calculations or regular expressions,
identification of relations using lexico-syntactic pat-
terns or proximity computation.
TERMINAE. TERMINAE is a software platform
supporting the development of terminology and on-
tology from texts (Aussenac-Gilles et al., 2008). This
tool integrates a terminology learning environment,
an environment to assist the conceptualization and an
ontology management system. Like Text2Onto, TER-
MINAE stores the link between the designed ontol-
ogy and the texts. This link takes into account linguis-
tic phenomena like polysemy or synonymy, and keeps
track of designer choices about the organizationof the
ontology hierarchy.
The approach suggested in this paper has been ap-
plied to the DAFOE platform that also ranks among
the tools for supervised design of ontologies and fo-
cuses on setting the transition rules from one step of
modeling to another. The setting process is based on
model mapping strategies.
Indeed, Text2Onto and TERMINAE assume that one
well knows in advance in which concept of the next
step a concept of the previous step will be transformed
(e.g., a Class resulting from a Term). In DAFOE, this
matching assumption is not done. Thus, a mapping
space is needed to represent transformations so that
mapping could be easily improved.
3 PRELIMINARIES
This section provides a terminology with definitions
used in this paper, explain the goal of mappings and
presents the EXPRESS modeling language that we
used to formally implements our approach. Some of
these definitions are borrowed from the ontology do-
main (de Bruijn et al., 2004) and are generalized for
models in general.
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