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.

Keywords:

Ontological View, Extensional Semantics, Intensional Semantics, Semantic Integration.

Abstract:

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.

1 INTRODUCTION

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

109

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.

2 MOTIVATION

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-

plete).

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-

sented.

• 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.

3 BACKGROUND AND

LITERATURE REVIEW

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

110

cally independent of its implementation. It is mainly

a syntactic-based integration, with highly implicit un-

derlying semantics within the structure and the de-

sign.

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,

2004).

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

111

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.

4 PROPOSED SOLUTION : A

FORMAL MODEL

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

reasonable:

• 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

model.

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.

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112

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

tuple:

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

world.

• 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-

spective.

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-

lows:

Ontological View-driven Intensional Semantic Integration for Information Systems in a Decentralized Environment

113

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.

• ρ

: 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

ρ.

• 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 >.

• S

C =< D, R

C > is the intended extensional

structure of w according to C.

– R

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

Formulation

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

models.

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, ℑ >

where:

• C is a conceptualization.

• ℑ is an intensional interpretation function assign-

ing elements of D to constant symbols of V , and

elements of R to predicate symbols of V .

ℑ : V → D ∪ R

In order to restrict the intensional semantic level Θ,

utilized by ℑ, 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

if:

• S

C ∈ SC;.

• ∀c ∈ V : I(c) = ℑ(c).

KEOD 2022 - 14th International Conference on Knowledge Engineering and Ontology Development

114

• ∃w ∈ W ∀p ∈ V : ℑ(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

(L).

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

(L)

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

view.

4.5 Ontological View Completeness and

Soundness

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

Formulation

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

ΘA

(L)

and intended extensions I

ΘB

(L), respectively, which

overlap.

Figure 8: Scenario (a).

In scenario (b) as in Figure 9, intended extensions

ΘA

(L) and I

ΘB

(L) do not overlap.

Figure 9: Scenario (b).

The semantic integration is feasible if its intended

extensions I

ΘA

(L) and I

ΘB

(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

115

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

generality.

• For semantic integration, all information systems

of consideration are associated with ontological

views that are committed to overlapping intended

extensions.

• 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.

5 CONCLUSIONS

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-

vironments.

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