Ontological View-driven Intensional Semantic Integration for
Information Systems in a Decentralized Environment
Fateh Mohamed Ali Adhnouss
, Husam M. Ali El-Asfour
, Kenneth McIsaac
, Idris El-Feghia
Raafat Aburukba
and Abdulmutalib Wahaishi
Dept. of Electrical & Computer Engineering, University of Western, London, Canada
Faculty of Information Technology, Misurata University, Libya
Department of Computer Science and Engineering, American University of Sharjah, Sharjah, U.A.E.
Software Engineering, Rochester Institute of Technology, New York, U.S.A.
Ontological View, Extensional Semantics, Intensional Semantics, Semantic Integration.
Ontologies are an essential component of semantic integration approaches for information systems . In a
decentralized environment, each specification of the domain reflects an Ontological view. However, the se-
mantics characterization of information systems in such decentralized environment poses a significant issue
related to their integration. Information systems are viewed as independent intensional entities with their own
beliefs, distinct from those held by others. Such autonomy is distorted by traditional extensional semantics.
Other entities’ beliefs are introduced into a given entity, thus affecting their beliefs. Additionally, the informa-
tion that one entity provides to another entity may not be consistent with the information known by the latter.
We need an alternative semantics for information integration, which is not dependent on the extension, but
rather on the underlying conceptualization. This paper proposes a classification of the environment where the
information systems lives and a novel modelling paradigm for information integration using intensional logic
to model the ontological views. The model comprises a formal modeling approach for the conceptualization
as well as for the semantic integration process.
Semantic integration based on ontologies is an essen-
tial category of solutions to the semantic integration
problem. However, the ontology may only be agreed
upon in a closed environment. When different in-
formation systems designers face the same domain,
each will have a specific view of the domain regard-
ing their interest, which results in multiple models.
(Xue et al., 2012). Since the domain can be viewed
in various ways, there is not merely one unique “on-
tology” for it. Instead, each view can be formally and
explicitly specified, and we define the corresponding
specification as an ontological view (Ov). Usually,
there are no explicit ontologies within information
systems. Rather, the associated semantics are implied
within the supporting information model (Wang et al.,
2009). The information model reflects a specific view
of the conceptualization that implicitly defines an Ov.
Semantic integration approaches for information sys-
tems based on the extensional model are inadequate
for a decentralized environment (Majki
c and Prasad,
2018; Ali and McIsaac, 2020). This is because they
do not account for the dynamic nature of a decentral-
ized environment. The dynamic nature of an environ-
ment can be described as a structure using the set of
entities present in the environment and the relations
between them. The relations between the entities may
vary. There is a need for an adequate semantic inte-
gration model to account for the decentralized envi-
ronment to capture the variations of the relations be-
tween the entities (dynamic nature) and the changes
that may occur in the relations among entities that ex-
ist in the domain of interest. This paper discusses con-
ceptualization and modeling languages based on envi-
ronments classification and proposes a classification
of conceptualization and a novel modeling paradigm
for information integration using intensional logic to
model the ontological views. The model comprises a
formal modeling approach for the conceptualization
as well as a formal modeling view for the semantic
integration process.
Additionally, the work focuses on conceptualiza-
tion and Ov models to facilitate semantic integration
Adhnouss, F., El-Asfour, H., McIsaac, K., El-Feghia, I., Aburukba, R. and Wahaishi, A.
Ontological View-driven Intensional Semantic Integration for Information Systems in a Decentralized Environment.
DOI: 10.5220/0011379200003335
In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 2: KEOD, pages 109-116
ISBN: 978-989-758-614-9; ISSN: 2184-3228
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
for information systems in a decentralized environ-
ment. The investigation of the extensional, extension
reduction and intensional formal models for concep-
tualization to account for a decentralized environment
is the central focus of the work presented here.
The rest of this paper is organized as follows: Sec-
tion 2 explains the motivation for the research. Sec-
tion 3 presents a brief background and a literature re-
view relevant to our research. Section 4 proposes a
classification of the environment, followed by theo-
retical foundations for conceptualization and ontolog-
ical views for semantic integration. The last section of
the paper provide conclusions and directions for our
future research.
Within the Ov, there can be very lightweight lan-
guages at the representation level that may consist of
terms only, with little or no explicit specification of
conceptualization. On the other hand, there are rig-
orously formalized logical theory-based approaches.
We provide a formal way to explicitly specify the con-
ceptualization with rich details based on various in-
formation models, utilizing logical language with a
more explicit specification of conceptualization. This
can be done by investigating the nature of the en-
vironment, providing an adequate conceptualization
structure, and identifying the conceptualization struc-
ture’s conversion into a representation. As such, We
focus our attention on conceptualization and Ov mod-
els to facilitate semantic integration for information
systems in a decentralized environment. This can
be done by investigating the nature of the environ-
ment, providing an adequate conceptualization struc-
ture, and identifying the conceptualization structure’s
conversion into a representation. More specifically,
this research establishes a foundation for semantic in-
tegration in a decentralized environment to address
the following issues:
The adequate structure of conceptualization.
The adequate representation language to provide
the manifestation of conceptualization.
The correctness of the model (sound and com-
Motivated by the aforementioned issue, the work
presents a semantic based Integration model that
is adequate for decentralized information systems,
where the Ov is the basis and the following are the
underpinning foundation pillars:
Formal modeling of conceptualization: This in-
volves investigating the nature of the environ-
ment, its classifications, the various conceptual-
ization approaches (extensional, extensional re-
duction, and intensional) and their supporting lan-
guages (intensional logic, extensional logic). Ac-
cordingly, a formal model for conceptualization
to account for a decentralized environment is pre-
Formal modelling of semantic integration: The
modelling of the conceptualization should be the
basis for the integration of various heterogeneous
information systems. This integration will be
derived by the mapping between the ontological
views of the information systems.
A tremendous amount of previous research has been
devoted to providing various semantic integration so-
lutions. This section outlines some of the existing
approaches which have been applied to semantic in-
tegration solutions. Background concepts and views
will be provided, and various categories of these ex-
isting approaches will be discussed accordingly.
3.1 Semantic Integration
Multiple descriptions of the term ”semantic integra-
tion” have been developed. Taking human conver-
sation as an example, the heart of the semantic inte-
gration problem is how to determine when two state-
ments are about the same subject (Bhatia and Breaux,
2018). In some communities, this is known as the co-
referencing problem (Alfrjani et al., 2019). Seman-
tic integration, according to (Vetere and Lenzerini,
2005), is achieved by conceptual mappings that make
different datasets and process descriptions equivalent,
either pairwise or in relation to some (partial) unified
conceptualization. The conceptualization principle is
related to the “perception” of a universe within the
context of another.
A universe can be categorized with the context of
an environment it attempts to describe. An environ-
ment includes a set of “entities” and the “relationship”
between them.
In the literature, there are two basic semantic in-
tegration approaches, structural-based, and semantic-
based (Xue et al., 2012). In structural-based ap-
proaches, the integration is based on providing or gen-
erating a global structure that characterizes the un-
derlying conceptualization of the environment. The
global structure can be logically modeled and physi-
KEOD 2022 - 14th International Conference on Knowledge Engineering and Ontology Development
cally independent of its implementation. It is mainly
a syntactic-based integration, with highly implicit un-
derlying semantics within the structure and the de-
In semantic based approaches, integration is ob-
tained by sharing an equivalent conceptualization
among various information systems, which utilize the
underlying conceptualization of each system. The se-
mantic integration can be derived by identifying se-
mantic correspondences among a set of specifications
of conceptualization, given that multiple system con-
ceptualizations are derivable.
3.2 Ontology-driven Integration
Various definitions of ”ontology” have been pro-
posed. A basic definition of ontology is ”the speci-
fication of conceptualizations, used to help programs
and humans share knowledge” (Gruber, 1993). This
definition then evolves to ”a formal, explicit specifi-
cation of a shared conceptualization” (Guarino et al.,
2009). Another view of ontology is a hierarchy of
terms corresponding to “concepts” related by sub-
sumption relationships, such as things, events, and a
set of relations that are specified in some way in order
to create a shared equivalent conceptualization of “re-
ality” in an environment (McGuinness et al., 2000). In
the information management and knowledge sharing
areas, ontology typically defined informally as a set
of logical axioms describing the meanings of terms in
a particular community (Guarino et al., 2009).
On the level of ontology representation, research
has been conducted on the level of ”completeness”
and ”richness” of the ontology representation, as
shown in Figure 1 (Uschold and Gruninger, 2004). At
one extreme, there can be very lightweight represen-
tation languages that may consist of terms only, with
little or no explicit specification of conceptualization.
At the other end of the spectrum, there are rigor-
ously formalized logical theory-based approaches.In
this work, we treat ”ontology” as a formal specifica-
tion of conceptualization.
In this work, we view ”ontology” as a formal spec-
ification of conceptualization. In traditional ontology-
driven approaches, semantic integration is based on
sharing or deriving a common ontology among the
information systems. Two main models were dis-
cussed in (Sheth and Larson, 1990), namely: local-as-
view (LAV) and global-as-view (GAV). In global-as-
view approaches, entities and relationships in a global
ontology are defined as a view over system ontolo-
gies. In local-as-view approaches, entities and rela-
tionships in each system local ontology are defined as
a view over the global ontology. A major challenge
Figure 1: Spectrum of ontologies(Uschold and Gruninger,
is that in order to enable answering a query expressed
over the global ontology, it requires reformulating the
query in terms of queries to the local ontology. While
in the global-as-view approach such a reformulation
is guided by the definitions in the mapping. Yet, the
problem in this case is to support a reasoning step in
order to infer the ways the local ontology is meaning-
ful to the query.
3.3 Related Work
There was excellent work devoted to the definition
of conceptualization’s structure. To our knowledge,
there are three approaches that have been proposed in
the literature, namely, the extensional approach (Gru-
ber, 1993), the extensional reduction approach (Guar-
ino et al., 2009) and PRP approach (Bealer, 1979) .
Depending on the type of environment, each of these
approaches performs slightly different functions. The
extensional structure is suitable for describing a snap-
shot of the environment. Alternatively, the exten-
sional reduction structure (possible worlds structure)
is suitable for conceptualizing a dynamic environment
in which the relationships between the entities are free
to change.
(Xue et al., 2012) attempt to address semantic in-
tegration and mapping between ontologies in a decen-
tralized environment. The primary focus was on se-
mantic equivalence relationship discovery. For con-
ceptualization, the author employed possible worlds
structure. The author also used the schema-matching
approaches to deal with the issue of heterogeneity.
However, semantic equivalence relationship discov-
ery based on only syntax and representation language
is extensional in nature.
In (Wang et al., 2009), heterogeneity, autonomy,
and distribution are addressed. This work proposes
a framework for semantic transformation in decen-
tralized systems. Conceptualization structure of the
work is based on the Possible Worlds formal model.
However, the work does not address the representa-
Ontological View-driven Intensional Semantic Integration for Information Systems in a Decentralized Environment
tion language aspect, instead providing a definition of
the ontological commitment through the use of an ex-
tensional representation language.
The work in (Ali and McIsaac, 2020), proposed
an intensional model for the conceptualization. This
model is based on the theory of Properties Relations
and Propositions (PRP) proposed by (Bealer, 1979).
Intensional based modeling is an adequate and natural
choice for modeling in an open environment. How-
ever, the focus was on the conceptualization structure
aspect, and it does not address the formal specifica-
tion of a conceptualization as it is the main element
for the ontology and ontological views based on the
definition of ontology.
As the objective is to provide theoretical and practical
foundations for developing sound engineering solu-
tions for ontological view-driven semantic integration
in decentralized environments, we stress the follow-
ing assumptions which are considered practical and
For semantic integration, all considered informa-
tion systems are associated with the Ov that is
committed to overlapping intended models.
For each information system, there is an explicit
specification of conceptualization, such as schema
that is used to organize the system’s data and con-
vert the data into information.
4.1 Semantic Integration: Foundation
and Core Principle
It is noteworthy that, in our work we focus on aspects
related to the conceptualization of the “Universe”. A
Universe can be thought of in terms of an environment
in which the existence of its “entities” is governed by
rules and axioms (Stevenson, 2010). Within this con-
text, we classify a particular environment as follows:
Closed environment (C E): where the existence of
the entities is predefined.
Open environment (OE): where the existence of
the entities may vary.
Furthermore, CE, can be further labeled as either
being static or dynamic. In a static closed environ-
ment (SCE) the entities and their relevant relation-
ships are invariant and predefined in priori. In con-
trary, in a dynamic closed environment (DCE), the
entities are predefined, but the relationships are muta-
ble and may vary. Whereas, in OE, both the existence
of the entities and the relationships are mutable and
might vary unpredictably (i.e., an entity may join or
leave the environment). Figure 2 below illustrates the
different categories of the environments and the rele-
vant elements.
Figure 2: The essential elements of the environments.
In the context of information systems, data is a
set of computational symbols that can represent ex-
tensional entities and relationships corresponding to a
given conceptualization, within an observed universe.
Whereas, the specification of a conceptualization,
such as a schema, is a semantic level model of a con-
ceptualization which uses a specific language. Typi-
cally, in information systems, semantic level model-
ing is implied in the design and the structure of the
In this work, the semantic level modeling of infor-
mation systems is represented in terms of “concep-
tualization structure”, “language” and “the specifica-
tion of a conceptualization”. We view conceptualiza-
tion as an abstraction of the observed universe. The
specification of conceptualization using a specific lan-
guage Ov represents an approximate intended mode
as shown in the Figure below. The conceptualization
space defines the conceptualization structure, the rep-
resentation language and the Ov.
Consequently, we classify the semantic level mod-
eling in terms of the type of the associated environ-
ment of the observed universe. A universe associated
with an environment can be described using a con-
ceptualization structure and supporting language, as
depicted in Table1.
In order to support semantic level modelling, it is
essential for both the language and the conceptualiza-
tion structure to be correct in the sense that it is com-
plete and sound. In other words, the language and
structure should satisfactorily model all the inherent
elements of the environment. The completeness en-
sures that all intended models can be generated by the
Ov. While the soundness ensures that any model gen-
erated by the Ov is an intended model.
KEOD 2022 - 14th International Conference on Knowledge Engineering and Ontology Development
Figure 3: The relationships between reality, conceptualiza-
tion structure, the representation language and an Ov.
A particular universe can be described in terms of
conceptualization structure which includes a set of en-
tities present in the environment and the set of rela-
tions between them.
Therefore, a conceptualization ( C ) is defined in
terms of the the set of the possible worlds (W ), the set
of entities (D) and intensional relations (R).
In a universe of a static closed environment (SCE)
where entities and the relations remain constant, the
conceptualization is defined in terms of the following
C =< D, R >
(Gruber, 1993)
In a universe associated with a dynamic closed
environment (DCE), the conceptualization takes into
consideration the possible worlds in which the varia-
tions of the relations between the entities can be mod-
elled using possible worlds theory and thus is defined
per the following tuple:
C =< D,W, R >
(Guarino et al., 2009)
A universe associated with an open environment
(OE) can be described utilizing intensional algebraic
structure is defined in terms of the following tuple:
C =< D, t, K >
(Bealer, 1979)
As for the modelling language, logic-based lan-
guages are complete, as such:
FOL as extensional language is complete and
sound language for SCE. The extensional model
describes the environment in terms of declara-
tive sentences and ordinary relations. The exten-
sional model is appropriate for describing a closed
FOL utilizing modal operators (necessity and pos-
sibility) is complete and sound for the DCE. The
extensional reduction model is appropriate for de-
scribing a closed world in which the relations be-
tween the entities can change.
Intensional-based language can be complete and
sound for OE.
In the following subsections we augment a set of
definitions derived from (Guarino et al., 2009) as well
as (Wang, 2008)’s work that we deem necessary for
formalizing an Ov from a traditional ontology per-
4.2 Conceptualization: A Proposed
Formal Conceptualization Model
Given that < D, R > can relate to a particular Static
environment (a particular world), we will refer to this
as an extensional structure. SwC =< D, R
>. A
dynamic environment contains many of these world
structures, one for each world. Enumerating all the
possible world structures is not practical and is in fact
impossible. In other words, an extensional specifica-
tion of the conceptualization would require a listing
of all possible extensional structures. However, this is
impossible in most cases (e.g., if the universe of dis-
course D or the set of possible worlds are infinite) or
at least exceedingly impractical.
In light of this, it is more practical to introduce the
intensional structure as an extension of the concep-
tualization to include a dynamic environment. Vari-
ations in extensional structures can therefore be ex-
pressed in an intensional manner. Conversely, one
can extend the intensional structure into all possible
extensional structures. Here we present a novel con-
ceptualization based on the fusion of multiple exten-
sional structures, which are represented by Sw
D, R
>, Sw
=< D, R
>, and the intensional struc-
ture that is based on possible world, as shown in Fig-
ure 4. An attempt is made to abstract all extensional
structures into a generic conceptualization to encom-
pass all possible extensional structures.
4.3 Formal Modelling
In the proposed model, the conceptualization is repre-
sented by two levels of structures. On the higher level
is the intensional structure C =< D,W, R >, which
contains all possible worlds.
The lower level can be viewed as a function from
possible worlds into extensional structure sets such
as: Sw
=< D, R
>, ..., Sw
=< D, R
Therefore, we formally define the structure as fol-
Ontological View-driven Intensional Semantic Integration for Information Systems in a Decentralized Environment
Table 1: Conceptualization structure and supporting language describes a universe associated with an environment.
Environment Conceptualization structure Supporting language
SCE C= < D,R> First order logic FOL
DCE C= < D,W, R> Modal logic
OE C= < D,t, K> Intensional FOL
Figure 4: The conceptualization structure and its two levels.
: W
where ρ
is intensional relations are
defined on a domain space < D,W > where D is
a domain and W is a set of maximal extensional
structures of such a domain.
For a generic extensional relation ρ, Eρ ={ ρ(w) |
w W } will contain the admittable extensions of
A conceptualization for D can be now defined as
triple C =< D,W, R >, where R is a set of exten-
sional structures on the domain space < D,W >.
C =< D, R
C > is the intended extensional
structure of w according to C.
C = {ρ(w)|ρ R} is the set of extensions
(relative to w) of the elements of R.
SC is the set {S
C|w W } of all the intended
world structures of C.
C =< D, R >= S
C is the structure of the uni-
verse, in the extensional form. This is a direct
model for the structure of CE.
4.4 Ontological View: A Proposed
In order to comply with the conceptualization, we
present a formal treatment of ontological views in
terms of two distinct semantic levels, namely, inten-
sional (Θ) and extensional (Φ). Generally, inten-
sional semantics are broader than extensional seman-
tics. Hence, if one knows the intensional of an expres-
sion, one can determine its extension with respect to
a particular world.(Napoli et al., 2017).
Since the notion of a model is an extensional ac-
count of meaning (Guarino et al., 2009), a conceptu-
alization that is intentionally specified would necessi-
tate an ontological commitment to specify the inten-
sional meaning of the vocabulary and to constrain its
Figure 5: The relationship between semantic levels: inten-
sional Θ and extensional Φ.
In this context, if we consider an intensional struc-
ture < D,W, R >with an intensional language L, an
intensional interpretation and vocabulary V , we can
define the intensional semantic level of Ov, which
corresponds to ontological commitment in (Guarino
et al., 2009), as:
Θ =< C, >
C is a conceptualization.
is an intensional interpretation function assign-
ing elements of D to constant symbols of V , and
elements of R to predicate symbols of V .
: V D R
In order to restrict the intensional semantic level Θ,
utilized by , of the intentional logical language L to
be used in a manner intended for a specific domain
rather than randomly, an extensional interpretation I
accompanied by a set of axioms is required.
Now, given the intensional semantic level Θ and
an extensional interpretation I, an intended extension
(a model) M =< S
, I > of L , is compatible with Θ
C SC;.
c V : I(c) = (c).
KEOD 2022 - 14th International Conference on Knowledge Engineering and Ontology Development
w W p V : (p) = ρ ρ(w) = I(p).
Therefore, for a language L and conceptualization
C, the set of all extensions (models) of L that are com-
patible with Θ represents the set of intended exten-
sions I
(L) of L according to Θ.
The extensional semantic level Φ of Ov can then
be expressed as a specification of C formulated by a
language L, an extensional interpretation I and a set of
axioms that it and approximate the intensional inter-
pretation to the intended extensions (models) I
As a result, we can say:
Ov commits to C if it has been designed with the
purpose of characterizing C , and it approximates
the reality D through its extensions.
A language L commits to Θ if it commits to con-
ceptualization C such that Φ agrees with C.
L commits to Φ for a given Θ such that the I
is captured in the models for Φ.
Figure 6 below illustrates how the intensional se-
mantic level is derived from an intensional interpreta-
tion, and the extensional semantic level from an ex-
tensional interpretation to form an ontological view.
Figure 6: The relationships between the intensional seman-
tic level , the extensional semantic level and an ontological
4.5 Ontological View Completeness and
An essential aspect of semantic integration is the
degree of approximation of the intended extensions
(models). The level of the approximation of the in-
tended extensions is the extensional semantic level Φ
of Ov which is a logical theory accounting for inten-
sional semantic level Θ to a conceptualization of a
universe. An Ov indirectly reflects this commitment
(and the underlying conceptualization) by approxi-
mating these intended extensions. As illustrated in
Figure 7, if the Ov is not sufficiently accurate, and the
intended extension fails to meet the criteria of being
complete and sound we can assume that they are not
derived from the same conceptualization.
Therefore, in order to develop an ontological
view-driven semantic, Φ needs to be as accurate as
possible , otherwise, the ontological views will inter-
sect even though they are not semantically equivalent.
Figure 7: Ontological View Completeness and Soundness.
4.6 Semantic Integration: A Proposed
Utilizing the Ov formally, we view the semantic inte-
gration problem within the context of two scenarios.
In scenario (a) as in Figure 8, assume there are two
ontological views of different information systems A
and B for a common observed universe U . Assume
they are associated with intended extensions I
and intended extensions I
(L), respectively, which
Figure 8: Scenario (a).
In scenario (b) as in Figure 9, intended extensions
(L) and I
(L) do not overlap.
Figure 9: Scenario (b).
The semantic integration is feasible if its intended
extensions I
(L) and I
(L) overlap. Therefore, we
can conclude that Information systems A and B can be
semantically integrated if, and only if, their intended
extensions overlap and therefore we can assume that
they are derived from the same conceptualization of
the same observed universe.
Here we introduce what we call the semantic in-
tegration principle, wherein semantic integration be-
Ontological View-driven Intensional Semantic Integration for Information Systems in a Decentralized Environment
tween ontological views can be guaranteed if, and
only if, i) each is sound and complete, and ii) they
are associated with overlapped intended extensions.
Within the context of this research, we intro-
duce the following assumptions, which are considered
practical and reasonable. The objective is to provide
a practical foundation for the research and to help re-
duce the complexity of the problem without loss of
For semantic integration, all information systems
of consideration are associated with ontological
views that are committed to overlapping intended
For each information system, there is an ex-
plicit specification of conceptualization, such as a
schema, that is used to organize the system’s data
and convert the data into information.
Semantic integration approaches for information sys-
tems based on the extensional model are inadequate
for a decentralized environment. This is because they
do not account for the dynamic nature of a decentral-
ized environment. The dynamic nature of an environ-
ment can be described as a structure using the set of
entities present in the environment and the relations
between them. The relations between the entities may
vary. This work presented in the paper has outlined
ongoing research and proposed a new approach of
conceptualization classification and a modeling lan-
guage for information integration using intensional
logic to model ontological views. The investigation of
the extensional, extension reduction and intensional
formal models for conceptualization to account for a
decentralized environment is the central focus of the
proposed model. The intensional semantic model is
an applicable solution that utilizes an Ov suitable for
semantic integration that supports the deployment of
semantically enabled applications in decentralized en-
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