Mapping PLIB, OWL and Flight Ontology Models
Chimene Fankam, Yamine Ait-Ameur and Guy Pierra
Teleport 2 - 1 avenue Clement Ader - BP 40109 - 86961 Futuroscope Chasseneuil Cedex - France
Ontology model : PLIB, OWL, FLogic; Database; Mapping.
Ontologies are seen like the more relevant way to solve data understanding problem and to allow programs
to perform meaningful operations on data in various domains. However, it appears that none of the proposed
models is complete enough by itself to cover all aspects of knowledge applications. In this paper, we analyse
existing modeling approaches and classify them according to some revelant characteristics of knowledge mod-
eling we have identified. Finally, this paper present transformation rules between ontology models in order to
get benefits of their strengths and to allow a powerful usage of ontologies in data management and knowledge
Nowadays, ontologies are seen as a core technology
and a fundamentaldata model for knowledgesystems.
In this context, they are used in various research fields
and application domains. Several ontology models
using different languages or formalisms have been
proposed. These models have their own domain of ef-
ficiency (semantic web, integration, knowledge shar-
ing, database, reasoning, data exchange, etc.). It ap-
pears that, the language or the formalism used by
these proposals is influenced by the target domain, by
the difficulties to overcome and by the needs identi-
fied when defined. When taking into account these
parameters, the efficiency and computability of each
model should now be appreciated according to some
specificities of the context in which it has been devel-
oped (query answering, concept modeling, instance
storage, ...). However, two major problems arise: (1)
people may face the difficult problem of interoper-
ability since the constructs offered by each model are
different; (2) people may encounter some difficulties
to choose the right ontology model to use or to apply
in practical engineering areas since the objectives of
each model are different and context-dependent.
In several application domains and particularly in
the engineering area in which we are involved, con-
cepts and data are represented by classes and prop-
erties describing components with instances stored in
databases. The PLIB ontology model(ISO-13584-42,
1998), initially created for electronic and mechanic
components specification, catalogues storage and ex-
change, as well as the OntoDB(Pierra et al., 2005)
ontology based database, have been defined in this
area. PLIB ontologies allow to describe the knowl-
edge shared by engineers in their design activities. In
parallel, languages and models like OWL(Bechhofer
et al., 2004) and OWL Flight(de Bruijn et al., 2004c)
have emerged. These models target Web applications,
reasoning, workflow management, integration, elec-
tronic commerce, etc. in different application do-
mains. At this stage, we notice that each ontology
model has it own domain of efficiency and its own
targeted applications. Therefore, it seems necessary
to allow the possibility to use each model in an inte-
grated framework. Our work investigates the integra-
tion of PLIB and OntoDB models with other models
like OWL and OWL Flight in order to allow the con-
current usage of these models. This paper shows how
model transformations, using model mappings allow
to get the benefits of both of them.
This paper is organized as follow. We first discuss
the relevant characteristics of a modeling approach
from the expressiveness and database points of view
Fankam C., Ait-Ameur Y. and Pierra G. (2007).
In Proceedings of the Third International Conference on Web Information Systems and Technologies - Web Interfaces and Applications, pages 254-262
DOI: 10.5220/0001282302540262
in section 2. Then, we briefly describe the three ontol-
ogy models targeted by our work in section 3. Section
4 presents the mapping rules we have elaborated fol-
lowed by the description of how these mappings are
implemented in section 5. Finally, we provide some
conclusions and perspectives of this work.
This section outlines the weaknesses and strengths
of various ontology-based modeling approaches from
the expressiveness and database points of view. We
discuss some characteristics we have judged relevant
to distinguish ontology models. The choice of these
characteristics is based on similar studies comparing
OWL and OWL Flight(Patel-Schneiderand Horrocks,
2006; de Bruijn et al., 2004b).
2.1 Strong Typing
An ontology typically consists of a number of classes,
relations (usually called properties) between these
classes, instances and axioms. While translating an
ontology schema into a database, a class is usually
translated as a table while a property is translated to a
column of a given type in a table which corresponds
to the class in which it can be applied.
So, databases require strong typing modeling
since a property is linked to one class (its domain) and
is always associated with a type (its range) which can
be either a data type or a reference to class in case the
property defines a relationship between two classes
of the ontology. Some languages like those founded
on Description Logic(DL)(Baader et al., 2003) do not
impose strong typing. In such languages the problem
of satisfiability of concepts may arise. Thus, ontology
modeling must be submitted to a rigorous analysis in
order to avoid semantic errors due to unsatisfiable de-
scriptions. For example, given the class Person which
corresponds to a set of humans, and a property flavor
whose domain is not explicitly specified, the OWL
description intersectionOf(Person, Restriction(flavor,
value(acid))) is unsatisfiable since the property flavor
has no sense as a characteristic of the class Person al-
though it may have a sense for another class of the
2.2 Constraint
In a model, a constraint is a logical expression that
should always be valid (invariant). In many engineer-
ing area like e-commerce, constraints are important,
they are used to check the consistency of the knowl-
edge base(KB). In a KB, two types of constraint in
their implicit form are considered : property value
constraint (the range of the property in its definition)
and property cardinality constraint(with default value
one). Other constraints cannot be captured by the
classical knowledge base or the model itself; it is
the case for global constraints which hold on classes
rather than on properties. In those application do-
mains, the more an ontology model and the under-
lying formalism enable constraints specification, the
more it is appreciated by the engineers. However, one
should care about the evaluation of such complex con-
2.3 OWA vs CWA
OWA(Open World Assumption) and CWA(Closed
World Assumption) are two different ways of in-
terpreting data. CWA is typically applied in logic
programming and in database applications(de Bruijn
et al., 2004a). It assumes to have a complete knowl-
edge of the world which implies that if the fact C
is not a consequence of the KB, its negation is. So,
CWA deals with defaults and schema-data mappings.
Indeed, integrity constraints can be checked and val-
idated and data structure are typically closed. Some
critical domains of engineering like e-commerce are
inherently closed worlds.
When the world is assumed to be open, the logi-
cal operator TRUE and FALSE are not interpreted as
the strict negation of each other. It implies that a fact
C and its negation cannot be consequences of a KB
at the same time. OWA assumes incomplete informa-
tion by default. The fact that C is not a consequence
of the KB is not sufficient to assert that its negation is
a consequence of the KB. Thus, OWA supposes that
a KB can be intensionally underspecified in order to
allow others to reuse or to extend it. OWA is typically
used in the semantic web where the informationis dis-
tributed, in science where all the answers are not yet
known. In these areas, new assertions can be deduced
and, equivalences between facts can be inferred.
2.4 Context Modeling
Taking into account the context in which a class or
a property is defined is important while modeling a
domain. For example in mechanics, the resistance of
a hull depends on its temperature. This dependency
relationship among properties needs to be expressed
at the ontology level. Another point about property
is related to its value. Elaborated data type systems
Mapping PLIB, OWL and Flight Ontology Models
are required by real-world applications; the value of
the property temperature for example is meaningless
if it is not associated with an unit. In such domains, a
model cannot be used if it does not offer mechanisms
to capture such informations.
2.5 Inheritance and Instantiation
Inheritance is a powerful mechanism allowing an ob-
ject to have the same characteristics as another ob-
ject. Inheritance is usually declared at the class level.
This relationship between classes defines a class hier-
archy. In many languages(object-orientedand logics),
multiple inheritance is allowed, i.e., a class may be-
long to more than one inheritance hierarchy. Many
implementations of these languages including Java
and many data structure systems like XML Schema
support single inheritance only. Multiple inheritance
must then be used with care because it may lead to
integration problems due to the absence of explicit or
direct implementation. Relational database systems
like Oracle and Postgres which are more widespread
and efficient than the object database systems support
single inheritance only.
Instances correspond to objects or individuals de-
scribed by a class. The set of instances of a class de-
scribes its extension or population. An instance may
be an instance of a single class or of more than one in
case of multi-instantiation. In a database, an instance
is linked to one table only.
2.6 Reasoning
Reasoning tasks, like subsumption and classification
can be very useful in data integration. Subsump-
tion can assist in matchmaking classes and in finding
candidate classes to link ontologies. Class member-
ship inference and classification can be used to check
whether an individual is a member of a specific class
and to allow automatic migration of instances from
one class to another one. Other logical characteristics
like reflexivity, symmetry, ... can contribute to auto-
matic information filling, and improve query rewrit-
ing and thus answering.
For our overview, we have chosen three different
but overlapping ontology models : the PLIB model
targeting databases applications, OWL for semantic
web application and F-logic which handles procedu-
ral knowledge and addresses deductive databases and
the semantic web.
3.1 PLIB Ontology Model
The Parts LIBrary ontology model was developed to
describe technical and engineeringcomponentobjects
and to provide integration mechanisms. Components
objects are described by means of consensual con-
cepts of a target domain defined by their relevant
3.1.1 Main Characteristics
A PLIB ontology model is (Pierra, 2004) : (1) con-
ceptual i.e., each entity and each property are com-
pletely defined. The terms (or words) used for de-
scribing concepts are only a part of their formal def-
initions, (2) multilingual i.e., each entry is associated
with a globally unique identifier (GUI), words used in
some facets may appear in many languages, (3)formal
i.e., the PLIB ontology model is formally specified in
EXPRESS(Spiby and Schenck, 1994) and is unam-
biguous, (4) modular i.e., an ontology may reference
another ontology by an external reference, (5) consen-
sual i.e., the conceptual model of the PLIB ontology
reached an international consensus and has been pub-
lished as an international standard (ISO13584). Most
of PLIB domain ontologies are also published as in-
ternational standards (e.g., IEC1380:1998).
3.1.2 Formal Definition
Formally, (for a more complete description,
see(Pierra, 2004)) a PLIB ontology O is defined
as the 4-tuple O :<C, P, Sub,Applic >, where:
C is the set of classes describing the concepts of a
given domain;
P is the set of properties describing the instances
of C. It is assumed that P defines a much greater
number of properties than those usually repre-
sented and valued in the instances stored in a
database. Only a subset of them might be selected
for a particular database;
Sub is the subsumption function. PLIB imple-
ments two kinds of subsumption : is a which is
the usual single inheritance used to specialize a
class and is case of which is a particular imple-
mentation where properties are not inherited but
may be explicitly (and partially) imported from
another class.
Applic : C 2
is a map which associates to
each ontology class those properties that are ap-
plicable (i.e., rigid) for each instance of this class.
WEBIST 2007 - International Conference on Web Information Systems and Technologies
3.1.3 Modeling Fundamentals
The PLIB model adopts the CWA and supports other
capabilities including (1) strong typing : each prop-
erty is defined in the context of a class that consti-
tutes its domain, and it has a meaning only for this
class and its possible subclass(es); (2) context model-
ing : a property value may depend upon its evaluation
context (e.g., the weight of an object depends on the
gravity of the place where the object is located), the
property value may be associated with a measure unit;
(3) multiple points of view : an object may be charac-
terized by one single class, and it may be represented
by any number of discipline-specific ontology class,
the point of view itself being represented by an ontol-
ogy class; (4) it is also possible to define mathemati-
cal derivation among properties (e.g., the diameter of
a circle equals its radius times 2).
3.1.4 Usage
Nowadays, the PLIB ontology model is put into prac-
tice in various domains including integration of het-
erogeneous data sources and data warehousing(Xuan
et al., 2006). Modeling of engineering compo-
nent, electronic catalogues and Semantic Web(Aklouf
et al., 2003).
An efficient ontology based database architecture
associated with PLIB and named OntoDB is avail-
able. Several tests(Dehainsala et al., 2007) have
pointed out the effectiveness of this architecture com-
pared to other existing ontology-based databases like
RDF-Suite or Sesame.
3.2 OWL Ontology Model
The Ontology Web Language is a recommendation
of the W3C for expressing ontologies in the seman-
tic Web. It has been defined starting on the work on
RDF(Resource Description Framework), RDFS(RDF
Schema), DAML-OIL and DL. It offers reasoning ca-
pabilities over the ontology to check consistency and
to determine logical consequences (inferences).
3.2.1 Main Characteristics
OWL features several characteristics including (1)
multiple inheritance i.e., a class may belong of sev-
eral hierarchies, (2) property relaxation, unlike typical
propertiesin object-orientedprogrammingand frame-
based ontology languages, properties in an OWL on-
tology are not defined as a part of the class. Indeed,
OWL allows to defining a property without specify-
ing to which class it applies or in which set it takes
its values, (3) subproperty i.e., in OWL, subsump-
tion relationship is also declared at property level, (4)
sub-species i.e., the OWL language provides three in-
creasingly expressive sublanguages, OWL Lite, OWL
DL and OWL Full. Each of these sublanguages is an
extension of its simpler predecessor, both in what can
be legally expressed and in what can be safely con-
In the remainder of this paper, we are only con-
cerned with the most well-known and most investi-
gated specie of OWL, namely OWL DL.
3.2.2 Formal Definition
OWL DL has a direct model-theoretic semantics sim-
ilar to model theoretic semantics for DL. Its power
consists in its reasoning support built from sub-
sumption, classification, instantiation and satisfiabil-
ity. Note that all these reasoning are usually reduced
to DL (un)satisfiability.
Formally, DL languages typically have set-based
model-theoretic semantics, in which a concept C is
mapped to a subset of a domain
under an inter-
pretation I using a mapping function
. Similarly, a
role R is mapped to a binary relation over the domain
. Equivalence of concepts is interpreted as
equal subsets (C D is interpreted as C
= D
), sub-
sumption is interpreted as a subset relation (C D is
interpreted as C
), and so on. The semantics of
DL adopts the OWA.
3.2.3 Modeling Fundamentals
OWL ontologies may be non-canonical ontologies
because they allow derived concepts. Indeed,
OWL offers a large set of constructors allowing to
build: (1) descriptions specifying derived or defined
classes using logical operators (union, intersection,
complement), (2)restrictions, (3) logical characteris-
tics(reflexive, symmetric, transitive) of properties, (4)
relations on classes or on properties using equivalence
and disjunction axioms.
3.2.4 Usage
OWL is used by applications that need to discover
similar meanings represented through different de-
scriptions. Its main application domain is the seman-
tic web where it is used for describing concepts and
terms, for information retrieval and for interconnect-
ing web services.
Mapping PLIB, OWL and Flight Ontology Models
Frame logic is a deductive, object-oriented knowl-
edge information modeling language which combines
the declarative semantics and expressiveness of de-
ductive database languages with rich data modeling
capabilities supported by object-oriented data mod-
els. The basic idea of F-logic is to take complex data
types as in object-oriented databases and to combine
them with logic and the reasoning capabilities it of-
fers. The result is a powerful knowledge representa-
tion and query language.
3.3.1 Formal Definition
F-logic has a model theoretic semantics and a sound
and complete resolution based proof theory founded
on first-order predicate and on frame calculus(Kifer
et al., 1995). Formally, an F-logic theory is a set of
formulasbuilt using molecules. A molecule in F-logic
is one of the following assertions :
C : D expressing class membership (C is an in-
stance of D),
C :: D expressing subclass relationship (C is a
subclass of D), or
C[D E] expressing that for the individual C,
the property D has the value E.
In F-logic, the semantics is given in terms of sig-
natures and formulas satisfaction. Molecules are as-
sociated to classes via signature expressions which
specify a type constraint on the scope of a property
and the type of its value. A formula (including rules)
is defined by combining molecules with the usual
logic connectors , , , ¬, and quantifiers , .
3.3.2 Main Characteristics
F-logic characteristics include : (1) Unique Name
Assumption(UNA) i.e., any object in F-logic has a
unique identifier (its name) which is used to reference
it, (2) multiple inheritance, (3) strong typing i.e., F-
logic includes the definition of properties in the same
structure where the class is defined, (4) n-ary relation-
ship, on the contrary of most ontology formalisms,
where properties are binary relationships, F-logic ex-
presses n-ary relationships; a property is called either
attribute i.e., with no argument or method i.e., with
arguments, (5) polymorphism as in object-oriented
languages, (6) meta-modeling i.e., in F-logic, both
classes and instances belong to the same domain; thus
an object can be seen as a class or an instance depend-
ing of its role in a particular context, (7) presence of
rules that provide additional expressiveness on top of
the ontology via a flexible mechanism to add and to
derive implicit knowledge to and from the ontology in
efficient way.
3.3.3 Usage
F-logic is used in semantic web particularly for ontol-
ogy management, semantic web services, rule-based
extension of ontologies, software engineering, model-
ing of intelligent agents, knowledge representation in
a frame-based approach, and representation of object-
oriented databases.
3.3.4 OWL Flight
Having a rule language in extension of the ontology
language in order to allow efficient query answering
has been identified as a requirement for the semantic
web. OWL Flight has been proposed with rules ex-
tension for OWL.
OWL-Flight restricts OWL DL and extends the re-
stricted subset of OWL DL(de Bruijn et al., 2004a)
with a semantic based on logic programming with
F-logic semantic sugar rather than DL. It also bor-
rows the constraint-based modeling style common in
OWL Flight imposes several restrictions to
OWL DL. Indeed, complement classes, individual
(in)equality, the usage of property restriction are some
of theses limitations. However, it provides with ex-
tensions the restricted subset of OWL DL by adop-
tion the data type formalism of OWL-E(Pan et al.,
2004), UNA, adding constraint constructs to object-
Properties, Local CWA (LCWA is applied in order to
enable constraint checking) and F-logic features for
3.4 Summary: Strength / Weakness
Sections 3.1, 3.2 and 3.3 showed three main ontology
models. All these models describe a domain in terms
of classes, properties and individuals. Each of them
adds to this kernel some extra features (constructors,
axioms) enriching each system by specific useful fea-
tures but none of them includes all the characteristics
identified in section 2. All these characteristics are
important in knowledge intensive applications, they
should be provided by these knowledge models. In-
stead of creating a whole ontology model covering all
the characteristics, which is an unfeasible task, we
claim that each ontology model shall be used where
it is sufficient.
PLIB is most suitable for instance storage (canon-
ical classes, strong property typing). It has an
WEBIST 2007 - International Conference on Web Information Systems and Technologies
elaborated data type system but do not allow rea-
soning. The ontoDB tested architecture compati-
ble with other ontology models offers a persistent
OBDB for PLIB ontologies.
OWL DL offers a large number of constructors
allowing reasoning tasks but cannot express con-
straints, the modeling context of concepts or mea-
sure values.
OWL Flight combines the expressivity of OWL
and query answering by extending ontologies
with rules. It has a more elaborated data type for-
malism than OWD DL. It goes towards database
modeling with LCWA, constraints and property
typing but remains with non canonical concepts.
To conclude, we notice that while designing these
ontology models a compromise between expressivity,
instance storage and query answering has been made.
Table 1 summarizes the comparison of PLIB, OWL
DL and OWL Flight.
Table 1: Comparison of PLIB, OWL DL and OWL Flight.
Strong typing + + + - + +
Constraints + + + - + +
Context + + - - + -
Inheritance simple multiple multiple
Reasoning + - + + + +
We argue that in knowledge-based applications, all
aspects are important : (1) the domain must be well-
modeled; (2) individuals storage must be optimized
according to existing database management systems;
(3) the knowledge system must contribute to provide
database consistency and to offer a powerfulquery an-
As we have noticed in section 3.4, none of these
models is complete regarding to the knowledge mod-
eling characteristics of section 2 but it appears that
they cover all these characteristics. One may want for
example to switch from OWL to PLIB to get bene-
fit of a better database performance, but this switch-
ing supposes to re-encode OWL ontologies into PLIB
ones each time and requires to have a good knowl-
edge of all ontology languages. Therefore to get ben-
efit from the strength of the three models and exist-
ing tools, our proposal consists in defining a map-
ping between them. This mapping is either struc-
tural when we identify corresponding features in dif-
ferent models, or procedural when it requires a trans-
formation procedure allowing to transform a given
feature of the source model into another in the target
model. Notice that we present four mappings OWL
Flight allowing to go from one ontol-
ogy model to another. We have chosen to use PLIB as
a pivot model because it provides with efficient per-
sistency model through the OntoDB Ontology Based
DataBase (OBDB) model.
We have classified each transformation from a
model (the source) to another model (the target) into
one of the following three distinct groups :
Common kernel : contains features axiomatized
both in the source and in the target model. Given a
source’s feature of this group, there exists a com-
bination of target’s features which expressed its
semantics. It is the case for example of class hier-
archy and property’s cardinalities.
Encoding : is made of features which do not
exist in the target model but which can only be
represented by judiciously encoding some target’s
Not mapped : includes features which cannot
be classified in the two precedent groups (i.e., they
cannot be represented in the target model).
The remainder of this section presents mapping
rules between the different ontology models.
4.1 Mapping OWL to PLIB
This section describes the mapping OP from OWL to
PLIB. In the remainder of this paper, we manipulate
PLIB and OWL through their abstract syntax. OP(Q)
refers to the target PLIB entity on which the source
OWL entity Q is mapped. Table 2 shows that both
primitive and defined OWL classes are translated into
PLIB item class. The main difference between them
is that only primitive concept extensions are explic-
itly represented in PLIB-OntoDB while a view is as-
sociated to each OWL defined class to compute their
We have introduced an item class PLIB resource
LRC (Local Root Class), which is an item class re-
quired each time an OWL ontology is mapped. This
resource is used to constraint the type of OWL proper-
ties. As example, the scope (range resp.) of an OWL
property which cannot be deduced using property ax-
ioms or logical characteristics is set to LRC. We are
then ensured that there will be no semantic error even
in case of imported ontologies. When two ontolo-
gies are imported, the scope of their properties is re-
stricted by their respective LRC. Instead of reviewing
the whole OP mapping of table 2, let us review some
elements for each group defined in section 4.
Mapping PLIB, OWL and Flight Ontology Models
Table 2: Mapping OWL to PLIB.
Class(A partial ) Item class(A) is a LRC
Class(B complete ) Item class(B)
SubClass(A C
) OP(A) is a OP(C
SubClass(A C
) OP(A) is case of OP(C
), i6=j,
SubClass(A OP(A)(
Restriction(Q constraint(Q
allValuesFrom(C))) range(OP(Q)) = OP(C))),
OP(A)is a namescope(OP(Q)
comment definition
ObjectProperty (P Non dependent p det P(
) namescope(OP(A
) range(OP(A
), Cards(0, ?))),
OP(P) applic(OP(A
[symmetric] note(’NOTE&/symmetric/’)
UnionOf(A B)
Both OWL and PLIB offer support for simple
inheritance, the corresponding mechanism is called
either subclass in OWL or is a in PLIB. However,
multiple inheritance is not allowed in PLIB, but we
have expressed it by combining the is a and is case of
PLIB relationships. Also, OWL property logical char-
acteristics cannot be represented in PLIB, We propose
to use the PLIB meta data note filled with a predefined
formatted text (e.g., note : = NOTE&/symmetric/’
for a symmetric property) to encode property’s char-
acteristics. Thus, although property characteristics
are not axiomatized in PLIB, the predefined text en-
coded with the meta data note gives an essential in-
formation to understand their behavior. This infor-
mation will be later used by OntoDB to achieve spe-
cific actions (e.g., saturate the database in case of
symmetric properties). Some OWL constructs like
UnionOf(. . .) cannot be mapped to PLIB. Notice that
in OntoDB, OWL individuals are converted and rep-
resented when possible on their canonical form. For
example an instance of the OWL class Restriction (P
...) will be stored like an instance of the PLIB class
OP(domain(P)), while an instance of the OWL class
UnionOf(A,B) will not be stored in OntoDB.
4.2 Mapping PLIB to OWL
When mapping PLIB to OWL, the main difficulty to
avoid is the mapping of the rich PLIB data type sys-
tem and context expression.
OWL data type formalism is not expressive
enough to represent PLIB units. So they are trans-
lated to their basic type (see in table 3). We de-
cided to associate the OWL meta data comment filled
Table 3: Mapping PLIB to OWL.
Item class(A . . .) Class(A partial . . .)
A is case of C
Subclass(PO(A) PO(C
A(constraint(P Subclass(PO(A)
range(P)= C)) Restriction(PO(P)
Non dependent P D ObjectProperty(P
ET(P namescope(A) domain(PO(A))
range(C Cards(n,?))) range(PO(C))),
Unit type(T, u) Integer(T comment(’COM
definition(i) comment(’DEFINITION’i)
Dependent P DET(P)
with a predefined text each time a PLIB data type
is mapped. It will enable to further explain or clar-
ify the PLIB data type (e.g., comment := ’COM-
MENT&/unit&Symbol(u)/’ for a unit data type. More-
over, on the contrary of the mapping from OWL to
PLIB where the translation of constructors was injec-
tive, the mapping from PLIB to OWL is not injec-
tive. The meta-descriptor comment will therefore be
used to discriminate(make it injective). Notice that
for example, PLIB context-dependent property can-
not be mapped to OWL.
The detailed mapping from OWL to PLIB and
from PLIB to OWL is available in(Fankam, 2006).
4.3 Mapping F-logic to PLIB
As noticed in section 3.3.4, OWL Flight is a restricted
subset of OWL DL, so the translation rules from OWL
DL to PLIB still holds with OWL Flight. Moreover,
OWL Flight constraint axioms are translated in the
same way as OWL DL restriction axioms.
Table 4 shows the correspondence allowing to
map F-logic constructs to PLIB. Structural aspects for
classes and properties are translated like for OWL.
However F-Logic methods are translated into PLIB
specific properties with aggregates in order to han-
dle n-ary associations of properties to classes. We are
currently investigating the mapping of other F-Logic
specific features as rules.
WEBIST 2007 - International Conference on Web Information Systems and Technologies
Table 4: Mapping Flogic to PLIB.
F-Logic PLIB
A[. . .] Item class(A . . .)
A :: C
FP(A) is a FP(C
A :: C
. . . FP(A) is a FP(C
A :: C
FP(A) is case of FP(C
i6=j, card(applic(FP(C
A[Q =>> C] Non dependent P DET(Q
domain(FP(C) Cards(0, ?)))),
FP(Q) applic(FP(A))
Datatype(T) Simple type(T)
head body
4.4 Mapping PLIB to F-logic
The translation of PLIB to OWL still holds for OWL
Flight for the same reasons previously mentioned.
The most important change concerns PLIB proper-
ties cardinalities which are translated to OWL Flight
constraint axioms. In addition, some PLIB user de-
fined data types and properties context definitions
and properties values context are translated by OWL-
E data type group. For example, the dependency
(1) : resistance = F(temperature) and the derivation
(2) : temperature in celcsius = temperature in kelvin
minus 273,15, will be expressed in OWL-E as
(1) restriction( resistance, temperature allTuplesSat-
ify(P1)) and (2) restriction(temperature in Celcsius,
temperature in kelvin allTuplesSatisfy(P2)) where P1
(P2 resp.) refers to the logical predicate correspond-
ing to (1) ((2) resp.).
Table 5: Mapping PLIB to FLOGIC.
PLIB F-logic
Item class(A) A
A is a C
PF(A) :: PF(C
A is case of C
PF(A) :: PF(C
Non dependent P PF(A)[PF(P) => PF(C)]
DET(P namescope(A)
Simple type(T) Dataype(T)
Unit type(T, u) Dataype(T)
A(constraint( ¬ ?o:PF(C)
range(P)= C)) ?x:PF(A)[PF(P) => ?o]
Non dependent P PF(A)[PF(Q)=>>PF(C)],
DET(P namescope(A) ¬less(length(?o), n)
domain(C Cards(n,?))) ?x :FP(A)[FP(P) ?o]
Dependent P DET(P)
Table 5 shows the mapping between PLIB and F-
logic. All PLIB constraints are mapped using F-logic
rules. PLIB data types are translated to their corre-
sponding basic type since F-logic only allows simple
data type.
4.5 Applying MDE Approach for
Our goal is to allow a systematic transformation be-
tween models. By applying rules at the ontology
model level, we are ensured that any ontology re-
sulting from the mapping of a source ontology (seen
as an instance of a source ontology model) pre-
serves the properties of its data along the transfor-
mation and is valid according to the target model.
The different mapping tables defined along this paper
are data-oriented descriptions. It is possible to im-
plement these mappings using model transformation
technique(Bernstein, 2003). Indeed, transformations
are implemented to map instances of data models en-
coding the meta model of the ontology models. The
EXPRESS language and its transformation language
EXPRESS-X is used for this purpose.
EXPRESS-X(ISO-10303-14, 1999) defines, man-
ages, and maintains traces and relationships between
different models. Like in other MDE techniques, the
transformationbetween models is expressed in a map-
ping schema called schema map. Transformations
can then be written in the form of a generic program
having as input the source model and the target model.
Table 6: An example of EXPRESS-X mapping.
SCHEMA source S; SCHEMA target S;
ENTITY student; ENTITY employee
name : STRING; name : STRING;
salary : optional REAL;
SCHEMA MAP example map;
TARGET target S;
SOURCE source S;
MAP st2emp AS emp :target S.employee
FROM st : source S.student;
WHERE (st.salary > = 0);
emp.name := st.name
emp.id := st.noSS
Table 6 shows an example of an EXPRESS-X
mapping. It defines two schemas a source (source S)
and a target one (target S) and a mapping schema
(example map). It maps student to employee by un-
folding the properties of student into the target entity.
Given the source instances of source S :
Mapping PLIB, OWL and Flight Ontology Models
#1 = student(
, $);
#2 = student(
, 3500) .
The resulting target instances of target S are:
#1 = employee(
The objective of this paper is to overcome the het-
erogeneity that results from different ontology models
which were defined for different purposes. We have
investigated the translation between ontology models,
and more precisely the integration of other ontology
models in the PLIB model and then in its OBDB On-
toDB. We have first proposed six relevant characteris-
tics(strong typing, constraint, WA, context modeling,
inheritance, reasoning) which are very important to
capture and to manage knowledge from the expres-
siveness and database points of view.
We have presented and compared with respect
to these characteristics three ontology models PLIB,
OWL DL and OWL Flight by emphasizing for each
model on its main characteristics, its formal seman-
tics, its modeling fundamentals and its usage. We
summarized this comparison in table 1. With respect
to this comparison, we concluded that none of these
three ontology models is complete. We therefore ar-
gue, according to the fact that knowledge applications
require and combine all the previously mentioned
characteristics, that an automatic mapping and/or a
procedural translation must be defined upon these
models. By adopting PLIB as the central model for
these mapping, we have provided an efficient man-
agement architecture compatible with other ontology
models (OWL) in addition to the features which will
enrich PLIB through the mappings.
We have identified a number of remaining impor-
tant issues to extend this work, including: (1) Investi-
gate the efficiency and the complexity aspects of this
mapping. (2) Enrich the PLIB central model to for-
mally represent non canonical expressions and fea-
tures which are now translated using meta data. (3)
Allow the definition of new meta classes in OntoDB
in order to support specific features of other ontology
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