A Survey on Conceptual model of Enterprise ontology

Abstract

Enterprise ontology serves as a foundational framework for semantically comprehending the nature of organizations and the essential components that uphold their integrity. The systematic and conceptual understanding of organizations has garnered significant attention from researchers due to its pivotal role in various domains, including business modeling, enterprise architecture, business process management, context-aware systems, application development, interoperability across diverse systems and platforms, knowledge management, organizational learning and innovation, and conflict resolution within organizations. Achieving a consensus on the concepts related to the fundamental elements that constitute an organization is therefore critical. This study aims to conduct a comprehensive analysis and comparison of existing conceptual models of enterprises as documented in scholarly articles published over the past decade. We discuss the strengths and weaknesses of each model and introduce a robust framework for their evaluation. To facilitate this evaluation, we propose several pertinent criteria derived from established methodologies for assessing ontologies. Furthermore, we identify contemporary challenges and issues that have been overlooked in prior studies, offering insights and suggestions for future research directions in enterprise modeling. This article ultimately presents a roadmap for enhancing the systematic understanding of organizations through refined enterprise ontology frameworks.

Full text

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A Survey on Conceptual model of Enterprise ontology
Zeinab Rajabi, Assistant Professor, Faculty of Computer Engeenering Department, Hazrat-e Masoumeh University,
Qom, Iran, z.rajabi@hmu.ac.ir)corresponding author (
Seyed Mohsen Rahnamafard, University of Tehran,Tehran, Iran, rahnamafard@alumni.ut.ac.ir
Abstract:
Enterprise ontology serves as a foundational framework for semantically
comprehending the nature of organizations and the essential components that
uphold their integrity. The systematic and conceptual understanding of
organizations has garnered significant attention from researchers due to its pivotal
role in various domains, including business modeling, enterprise architecture,
business process management, context-aware systems, application development,
interoperability across diverse systems and platforms, knowledge management,
organizational learning and innovation, and conflict resolution within
organizations. Achieving a consensus on the concepts related to the fundamental
elements that constitute an organization is therefore critical.
This study aims to conduct a comprehensive analysis and comparison of existing
conceptual models of enterprises as documented in scholarly articles published
over the past decade. We discuss the strengths and weaknesses of each model and
introduce a robust framework for their evaluation. To facilitate this evaluation, we
propose several pertinent criteria derived from established methodologies for
assessing ontologies. Furthermore, we identify contemporary challenges and issues
that have been overlooked in prior studies, offering insights and suggestions for
future research directions in enterprise modeling. This article ultimately presents a
roadmap for enhancing the systematic understanding of organizations through
refined enterprise ontology frameworks.
Keywords: Enterprise ontology, Conceptual model, Enterprise dimension,
Enterprise architecture
1-Introduction
Throughout the literature, scholars have provided numerous definitions for the term
organization. Most define an organization as a group of individuals who have come
together to achieve a specific goal[1] . An organization is a phenomenon whose most
important constituent is human beings. According to postmodernist beliefs shared by
several philosophers over the past couple of decades, enhancing interactions among
humans is one of the most important areas for achieving high-performance
organizations[2]. One relevant topic in this area is semiotics, which concerns interactions
formed through signage and sign processes, including modeling languages[3].

2
On the other hand, according to systems theory, an organization is also a kind of one of
the many systems around us. Most scholars whose ideas are based on Newtonianism and
systematic thinking believe that an organization is a system consisting of elements
between which distinct relationships exist; thus, understanding organizations depends on
grasping these elements and their relationships[4]. Despite these scientific approaches,
there is still a lack of a common language that would help bring about a general consensus
in the research community[5].
Management scholars have developed models to better understand organizations, their
organizational problems, and to help classify and interpret their data systematically; this
was presented by Rock and Crawford. Some studies such as [6],[7],[8],[9],[10] and model
the relationships between organizational components. For example, Leavitt[11], in his
diagnostic organizational model, introduces organizational components such as structure,
technology, people, and tasks. Weisbord presents a six-box organizational cognitive
model[12], [9] , which includes purposes, structure, relationships, rewards, leadership,
and helpful mechanisms. Janicijevic[13] examines and compares the organizational
cognitive model, dividing organizational elements into two categories: static and
dynamic. Static elements include organizational structure, systems, culture, informal
groups, and power structure, whereas dynamic elements are business processes, group
processes, leadership, conflicts, political processes, and communication. After reviewing
the literature, he concludes that existing diagnostic organizational models are imperfect
because they do not include dynamic formal organizational components such as business
processes. According to him, a complete and comprehensive diagnostic model should
encompass business processes and related parameters such as process owners and
participants, organizational competence, key performance indicators, business process
shortcomings and problems, key paths to change business processes, and business process

3
priorities.
On the other hand, information technology researchers want to align information
technology with the goals of the organization and also develop information systems after
the organization’s structured recognition. Artificial intelligence researchers also seek to
understand the structure of organizations with the aim of creating a suitable environment
for their own systems. Ontology is the most relevant area of science that aims to recognize
these aspects of organizations. This field was first introduced by artificial intelligence
experts in order to make sense of human semantic treasure for machines. The field of
ontology has developed methods and tools to build different conceptual models for verbal
and non-verbal concepts in various subject domains, one of which includes that of
organizations. Roseing[14] reviewed business ontology research and examined how
business ontology is used in organizational development. He used the potential of
ontology and semantics to develop standards that describe objects, relationships, and rules
for enterprise modeling, organizational engineering, and enterprise architecture.
Enterprise ontology provides a uniform representation of similar semantic content[15].
Modellers use different methods to develop models. These models are created with
different languages and modeling tools. There may even be various styles and different
techniques used within a single method. In addition to this, products created by different
organizations and disciplines use different terms to analyze organizations, which leads to
various perceptions of the organization. Therefore, a standardized format is needed to
translate data among different systems of the organization and to understand different
models of organizational analysis[16].
Enterprise ontology provides a data structure that facilitates the reader’s understanding of
data usage in an organization description document. For example, Rajabi et al. [17]

4
present the methodology for enterprise architecture development based on enterprise
ontology. The ontology of the enterprise provides the necessary information to collect,
organize, and store data in an easy way to understand[18] [19]. For example, the Dodaf
Data Meta Model[20] states that the goal of a conceptual model is to support the integrity
and semantic accuracy of architectural descriptions.
On the other hand, enterprise ontology helps to model more efficiently by describing
enterprise building blocks and their relationships. The enterprise ontology is a proper
basis for an integrated understanding of an organization's elements. The enterprise
ontology actually models the building blocks of organizations with their relationships
according to the perception of entities from two parties[16]. The relationships among all
elements of the organization are modeled precisely, transparently**,** and are
formulated in the ontology of the organization; then a common model is created that has
the necessary precision for all parties within the organization and systems.
It is quite clear for researchers the advantages and successful applications of ontology in
business and various applications[21]. Ontology development for organizations is the
proper basis for enterprise architecture methods[17],[19],[22] automatic analysis of
models at enterprise architecture, querying and inference in architectural data[23],
business process management[24],[25],[26], business modeling[27], business process re-
engineering[24],[28], implementation of applications[29], context-aware systems[30],
interoperability between different systems and platforms[31], knowledge management in
the organization[29], and so on. Therefore, it is of paramount importance to identify a
suitable ontology that has the necessary comprehensiveness, proper coverage, accuracy,
compatibility, and extensibility for several applications.
This study aims to evaluate and compare enterprise ontology models from the conceptual
view and then analyze their results. O'Leary[32] reviews enterprise ontology according

5
to activity theory but doesn't consider many other aspects of enterprise ontology. Besides
O’Leary’s work, it could be said that there are no other proper comparisons and
classifications of enterprise ontology models available. Therefore, researchers who need
to use enterprise ontology models in different domains may become confused as the
domain of relevance of each model is not clear.
In this paper, a framework is presented to compare enterprise ontologies at the conceptual
level. Then, the criteria for the evaluation of ontology are studied, and appropriate criteria
relating to the conceptual level are selected among them. Subsequently, we compare and
examine enterprise ontology models using the said criteria. Finally, we analyze the results
and provide a roadmap for future research on conceptual models of enterprise ontology.
Section 2 reviews related works, section 3 examines conceptual models of enterprise
ontology, section 4 analyzes the results of surveys, and section 5 concludes the discussion
by providing suggestions for future work.
2-Related work
2-1-Ontology
According to the Gruber definition in 1993[33], an ontology is a formal, explicit
specification of shared conceptualization. On the basis of this definition,
"conceptualization" refers to an abstract model of phenomena in the world along with the
detection of related concepts to those phenomena. “Explicit” means that the types of used
concepts and their limitations are defined explicitly. “Formal” refers to the fact that the
ontology should be readable to a machine and “shared “indicates that the ontology must
acquire agreed and acceptable knowledge by related societies[23]. Although this
definition emphasizes the formal and explicit description of concepts, these descriptions
need to first agree on selected concepts and an acceptable conceptual model. If the
concepts aren’t chosen appropriately, the ontology usage will notbe efficient. Awell-
defined conceptual model is useful in many researches and applications independently.

6
2-2-Conceptualization
A formal model is implemented in an ontology language such that the ontologist observes
a gradual transition from the knowledge level to the implementation level. The
formalization grade of the knowledge model increases gradually until it is able to be
understood by the machine. Figure 1 shows this gradual movement.
Ontology development activities generally include: specification, conceptualization,
formalization, implementation, and maintenance. Conceptualization is a crucial activity
in the ontology development process[34]. Some studies emphasize this and provide
methods for conceptualization. The conceptualization activity constructs meaningful
knowledge models from domain knowledge. The conceptualization activity is similar to
collecting puzzle pieces provided by the knowledge acquisition activity, and it is
completed during the conceptualization process. The conceptualization activity must be
done rigorously; otherwise, the error will propagate into the next steps.
The purpose of conceptualization is to prepare a domain model with a lower degree of
formality than a formal model but still more formal than the definition of the model in
natural language. Other motivations for the conceptualization process include:
1. Domain experts, human users, and ontologists may struggle to interpret or
understand the ontology proposed in the ontology language.
2. Domain experts may not be able to construct ontologies within their domain of
expertise.
This activity deserves special attention because it plays an important role in the ontology
development process. In ontology development methodologies, after the conceptual
model has been designed, the conceptual model is transformed into a formal model and

7
implemented in an ontology language such that the ontologist observes a gradual
transition from the knowledge level to the implementation level. The formalization grade
of the knowledge model increases gradually until it is able to be understood by the
machine. Figure 1 shows this gradual movement.
Figure 1: Knowledge in the Ontology Development Process [35]
T1 transformation refers to the conceptual modeling process that transforms the domain
expert’s subjective model into a conceptual model. T2 refers to the conceptual model
progressing into a formal model. T3 refers to the formal model progressing into a model
that can be understood by a machine. As the figure shows, T1 and T3 transformations are
drawn by a continuous line, while the T2 transformation is indicated by a dashed line.
This indicates that there may be some loss of domain knowledge when a conceptual
model is developed into a formal model. This happens when the components and tools
used to create conceptual models are more meaningful and expressive than those that are
used to create the formal model.
In the methodology development process[36], the conceptualization activity uses a set of
intermediate representations (IRs) through table and graph notation, organizing and
converting an informal representation of a domain into specifications that can be

8
understood by domain experts and ontology developers. In this study, we aim to compare
enterprise ontology models from a conceptual perspective.
2-3- Ontology-Based Enterprise Modeling
Enterprise ontology contains a set of well-defined terms that are widely used as common
descriptions of enterprises, as it accurately covers concepts related to the enterprise field.
The enterprise ontology acts as an interactive medium or platform between different
people, such as users, designers, and planners in various organizations[37].
An important issue in achieving integration and performing effective business planning
is that all operators and stakeholders (from planning managers to low-ranking contractors
involved in software production) must have a common understanding of the different
dimensions of an organization; when a particular word is used from the domain, the
concept it refers to should be clear. In other words, it is necessary to overcome the
“semantic heterogeneity” associated with implicit perceptions of common words’
meanings in the domain.
Enterprise ontology has been created for this purpose and contains a set of well-defined
words that are widely used as general descriptions of an organization, covering concepts
related to the domain of the organization carefully. This set facilitates a shared
understanding of an organization and can serve as a fixed basis for identifying functional
requirements and creating organizational models. Thus, perceptual errors are reduced in
cases where the same concepts may be referred to by different terms, as it improves and
facilitates interaction between stakeholders, which is an important step in increasing
efficiency.

9
The purpose of applying an ontology in an organization is to determine the relationships
between the tasks and activities of that organization with organizational knowledge and
their tools. It also participates in the acquisition, representation, and manipulation of
organizational knowledge, organized and structured libraries of existing knowledge,
rationalized descriptions of inputs and outputs of involved components, and a vocabulary
exchange format for an enterprise[38].
3- Investigating Conceptual Models of Enterprise Ontology
Researchers represent different ontology models for different applications. This article
selects pioneering research such as TOVE
1
[35], [39], context-based enterprise
ontology[40], and the enterprise ontology TEO
2
[37]. These projects and other
researchers are recognized as pioneers in this article. In addition, enterprise architecture
frameworks require a conceptual model of an organization. Therefore, some frameworks
and methodologies provide conceptual models of enterprise ontology for developing
enterprise architecture, such as the Dodaf
3
Data Meta Model[41], [42],[43], Modaf
4
[44]
, and the Togaf
5
Content Meta Model[45]. ArchiMate[45] is also investigated in this
study. Some researchers focus only on one dimension of an organization and present an
enterprise ontology model for it, as referred to in Table 1. For example, Almeida and
Gizzareti[49], Pereira and Almeida[45], Santos et al. [46], Abramowicz et al. [47], and
Pereira[48] have modeled organizational structure ontology and represented extensive
details. Some other studies construct an enterprise ontology model for a specific type of
1
Toronto Virtual Enterprise
2
Time Event Ontology
3
Department of Defence Architecture Framework
4
Ministry of Defence Architecture Framework
5
The Open Group Architecture Framework

10
organization. In this regard, Belo and Silva[49] have presented an enterprise ontology
model specifically for higher education institutions.
Table 1: Enterprise ontology which has examined the concepts of one of the dimensions of the organization
Dimension
References
Structure
[50],[46], [51],[45],[47], [48],[52]
Purpose
Business Motivation Model (BMM)[53]
Rules and Time
Date-Time Foundation Vocabulary Request For Proposal[54]
The best examples of such research can be found in the documentation provided by the
Object Management Group (OMG). Several OMG documents serve as valuable
references for analyzing the conceptual model of enterprise ontology, presenting concepts
in an organized manner and detailing their relationships with one another. One notable
document is the Business Motivation Model (BMM) [53], which offers a structured set
of concepts that model elements of a business plan. This document is particularly useful
for identifying the purpose and motivation of an organization, including concepts such as
purpose, mission, perspective, and strategy. Additionally, OMG has published a
comprehensive document on business process modeling known as BPMN
6
. This
document not only covers conceptual control flow modeling but also provides precise
definitions of components such as activities, events, gateways, and the sequences between
them. It also explores the relationships between various organizational components and
activities. For example, it represents capital resources using Pools and Lanes while
defining consumable resources with a symbol called the Data Object. Furthermore, the
Organization Structure Meta-model (OSM) [50] document from OMG offers
metamodeling for organizational structures. It includes modeling elements that represent
organizational entities, subgroups, features, and the relationships between organizational
units and their assigned individuals. The concepts used in organizational structure are
clearly defined within the OSM document. The Semantics of Business Vocabulary and
Rules (SBVR) [55] takes this a step further by expressing business vocabularies with
formal logic, providing a specific language for business descriptions. It defines a set of
terms, each with a specific technical meaning relevant to the field of business. The rules
defined by formal logic closely resemble natural language, making SBVR a business
ontology that machines can understand[55]. Lastly, the Date Time Foundation
6
[Https: //www. omg. org/spec/BPMN/2. 0/PDF]

11
Vocabulary Request for Proposal[54] is another OMG document that articulates concepts
related to time and dates in business using SBVR.
3-1- Providing a Framework for Comparison
There has been no existing framework to compare the conceptual models of enterprise
ontology until now, making our study the first to address this issue. In this regard, we
present a framework for evaluating conceptual models of enterprises. Two perspectives
have been considered in formulating this framework, which includes various comparison
parameters. The first perspective aims to identify the closest semantic frameworks to
enterprise ontology and draws inspiration from their parameters for comparison. The
second perspective identifies and applies general parameters for evaluating ontologies,
supported by detailed research in the field. In the first perspective, the most significant
research was conducted by Osterwalder[56], who presented a framework for comparing
business model ontologies based on earlier works[57],[58]. Our study generalizes
Osterwalder's framework to facilitate the comparison of enterprise ontologies. This
generalization seeks to provide an acceptable framework for comparing ontologies that
model organizations. In this framework, we introduce important parameters for the
comparison of enterprise ontology, which are described as follows:
1. Purpose: This parameter reflects the primary motivation behind enterprise
ontology and the objectives of its development. Several studies, such as[41] ,
present ontology models specifically for applications in enterprise architecture.
Others[59], like , concentrate on ontology models tailored for business contexts.
Additionally, some studies, such as[40], offer a more general enterprise ontology
aimed at enterprise modeling.
2. Domain: This parameter evaluates the domain of relevance for the conceptual
model. The enterprise ontology model is capable of representing various types of
enterprises, including business, military, and educational organizations.
3. Implementation language: This parameter indicates the programming languages
used to implement enterprise ontology and convert it into a machine-readable

12
format. It encompasses the use of generic ontological technologies for
representing ontologies, such as Ontolingua, RDF/S, and OWL, as well as
ontology design tools like Protégé.
4. Representation: Lightweight ontologies encompass concepts, classifications of
those concepts, the relationships between them, and characteristics that describe
each concept. In contrast, heavyweight ontologies build upon lightweight
ontologies by adding axioms and constraints that clarify the relationships within
the collected vocabularies. Heavyweight ontologies are typically implemented
using approaches based on artificial intelligence, employing first-order logic or
description logic. On the other hand, lightweight ontologies are represented
through software engineering methods, such as UML or database diagrams like
ERD.
5. Ontology content and components: This parameter refers to the key dimensions
addressed by each enterprise ontology. Each conceptual model of an organization
encompasses various dimensions and the concepts associated with them. It is
crucial to identify which dimensions are included at the macro level and which
concepts are most critical among all the concepts. Additionally, this parameter
takes into account the types of relationships and the nature of the rules present
within the ontology.
6. Ontology maturity and evaluation: The degree of maturity of an ontology is
determined by its evaluation. Various qualitative criteria and resources exist for
assessing ontological models that have been implemented, as detailed in
references such as [60], [61], [62] and . Several criteria possess significant
capability for studying the evaluation of conceptual models. In this article, we
select specific criteria to understand and compare the existing ontological models
of organizations. If the evaluation criteria function effectively at the conceptual
level, we can anticipate that the enterprise ontology will perform well at the formal
level. The selected criteria for evaluation are as follows:
a. Reusability: Reusability refers to the transferability of an ontology,
specifically which parts can be utilized to construct another ontology for
a different purpose. Given that ontology design and implementation can
be challenging and time-consuming, reusability is a crucial factor.

13
b. Accuracy: Accuracy requires that the underlying knowledge informing
the ontology aligns with the expertise of domain specialists. Ontology
models must accurately describe the real world, despite the inherent lack
of precise semantics that allows for various interpretations. Therefore, an
organizational ontology must demonstrate strong alignment with key
entities and correspond well with our understanding of the organization.
c. Expandability: Expandability refers to the ability to extend the ontology
into other domains without changing its definitions.
d. Adaptability: Adaptability reflects how well the ontology can anticipate
future developments. It assesses whether the ontology provides a solid
foundation that is easily expandable and sufficiently flexible to respond
predictably to minor internal changes. Unfortunately, many ontologies do
not offer an adequate basis for future expansion.
e. Completeness: Completeness examines whether the model has sufficient
domain coverage to enable the ontology to answer all relevant questions
within that domain.
The parameters are summarized in Table 2. This table, along with its parameters, provides
a framework for comparing enterprise ontologies, which we will discuss in this section.
Table 2: Ontology Evaluation Parameters That Are More Aligned with Conceptual Model Evaluation
Parameter
References
Description
Purpose
[56]
The main motivation of organizational ontology and the
purpose of creating the ontology.
Domain
[56]
The domain of organization which the ontology is modeling,
for example business, military or educational organization.
Implementation language
[56]
The implementation language and applied language used to
create the enterprise ontology.
Representation
[56]
How the the enterprise ontology model is represented.
Ontology content and component
[56]
The dimensions of the domain considered by the conceptual
model and the concepts underpinning them.
Ontology maturity
and evaluation
Reusability
[60]
The degree to which the entire ontology or part thereof can
be repurposed and reconstructed another ontology.
Accuracy
[7]
The degree of consistentency of the ontology with the
knowledge of a domain expert.
Expandability
[34]
The ability to extend the ontology to other domains in
without changing definitions.
Adaptability
[60]
Whether the model reacts predictably towards the small
internal changes or not.
Completeness
[7]
The ability to how exhaustively the ontology as answer all
questions that ontology should be able to answer.
3-2- Comparison of Enterprise Ontologies
In this section, we compare conceptual models according to our framework presented in
the previous section.

14
Purpose: The main motivation of organizational ontology and the purpose of creating the
ontology.The number of studies such as TOGAF Content model[63] [64] , ArchiMate
[45],[65] DODAF Data Meta Model[41], UAF
7
[66],[67] provided ontology models for
enterprise architecture applications. Some studies such as context-based[40] and The
Enterprise Ontology(TEO) [37] and TOVE[68] presented enterprise ontology in general
for enterprise modeling.
Domain: The domain refers to the specific type of organization that the ontology is
designed to model. This could encompass various sectors such as business, military, or
educational organizations. Each domain has its unique characteristics, structures, and
processes, which the ontology aims to represent accurately. By tailoring the ontology to
a particular domain, it becomes more relevant and useful for stakeholders within that
field, facilitating better understanding, communication, and decision-making.
TOGAF Content Model[63] [64], ArchiMate[65], and the Unified Architecture
Framework (UAF) [66],[67] are generic frameworks in the realm of enterprise ontology.
In contrast, other models, such as[59] presented in , focus specifically on ontology models
for the business domain. Additionally, the DODAF Data Meta Model[41] provides an
ontology model tailored for the military domain .
Implementation language: Most ontology models such as Dodaf Data Meta Model [41],
Togaf Content Model[63] [64], ArchiMate[65], UAF[66],[67] are represented at the
conceptual level by UML diagrams. TOVE[68] implemented by Prolog language and The
Enterprise Ontology(TEO) [37] implemented by Ontolingua language (based on KIF).
Most ontology models, including the DODAF Data Meta Model, TOGAF Content
Model, ArchiMate, and UAF, are typically represented at the conceptual level using UML
diagrams. In contrast, TOVE is implemented using the Prolog programming language,
while The Enterprise Ontology (TEO) is developed using Ontolingua, which is based on
KIF.
Representation: Most ontology models are implemented in a lightweight form, while
only TOVE and The Enterprise Ontology (TEO) [37] are classified as heavyweight
ontology models.
Content and component: The core conceptual model identifies the main dimensions of
an organization, followed by the detailed concepts that support each dimension. For
7
Unified Architecture Framework

15
example, The Enterprise Ontology (TEO) [37] represents five key dimensions: activity,
organization, strategy, marketing, and time.
Reusability: Most enterprise ontologies immediately transition into the implementation
phase without first establishing a solid conceptual model. This oversight limits users'
ability to connect the abstract concepts of the model with the real-world elements they
are intended to represent. A robust conceptual model is crucial for supporting reusability.
Among organizational ontology models, both TOVE and The Enterprise Ontology (TEO)
rush into implementation, leaving users without a comprehensive understanding of the
models, which hampers effective usage. In contrast, the DODAF Data Meta Model offers
a well-defined conceptual model that articulates relationships at a conceptual level,
although it is specifically tailored for the United States Department of Defense. The
Context-Based Enterprise Ontology starts with a conceptual level presentation, but many
of its relationships remain unclear, limiting its reusability. Meanwhile, the Unified
Architecture Framework (UAF) describes each concept precisely, supporting
extensibility; however, it suffers from ambiguities at the macro level, making reusability
challenging.
Accuracy: TOVE and TEO exhibit ambiguous concepts, with their definitions and
relationships not being clearly defined. This leads to varying interpretations of each
concept. In contrast, the DODAF Data Meta Model provides a well-defined enterprise
ontology aimed at representing the conceptual model of defense organizations.
Additionally, the UAF, which originated from DODAF and MODAF, is designed to
support non-defense organizations. The TOGAF Content Meta Model clearly defines
concepts and their relationships, offering a solid foundation for enterprise ontology
criteria; however, it does not implement the ontology model at a formal level.
Expandability: Context-based enterprise ontology, TOGAF Content Meta Model,
ArchiMate, and UAF are generally defined in a way that allows for good expandability
into specific domains. In contrast, the relationship between "activity" and "capability" in
the DODAF Data Meta Model is tailored specifically for military organizations, which
limits its applicability to other sectors despite its well-defined concepts.
Adaptability: TOVE and TEO transition abruptly into the formal phase without
adequately defining their concepts. On the other hand, the DODAF Data Meta Model
excels in defining the conceptual phase but is primarily suited for military organizations.
The TOGAF Meta-Model considers strong concepts at the micro level, yet it lacks a

16
comprehensive ontological structure.
Completeness: Completeness refers to how thoroughly an ontology can address all
relevant questions about the organization it represents. This means that the ontology
should cover all dimensions of an organization. Given the social nature of organizations,
researchers must consider multiple dimensions simultaneously. For instance, defining
“service” requires acknowledging the roles of both service customers and providers.
Similarly, a complete understanding of a “business process” necessitates describing the
roles of its participants. To achieve this, the structure of organizational units and the roles
within them must be framed within a broader organizational context. However, focusing
on too many dimensions can lead to selecting concepts that may not be essential for a
complete description of the domain. An ontology can be considered comprehensive if it
effectively answers questions such as: Who (performer) does what (task) for what reasons
(goal), where (location), and when (time) [40]?
Table 2: Comparison of Enterprise Ontology According to the Proposed Framework
TOVE
The
Enterpr
ise
Ontolog
y(TEO)
Context
-based
DODAF
Data
Meta
Model
TOGAF
Content
Model
ArchiM
ate
UAF
[68]
[37]
[40]
[41]
[64],
[63]
[45],
[65]
[66],
[67]
Purpose
Enterpri
se
modelin
g
Enterpri
se
modelin
g
Enterpri
se
modelin
g
Enterprise
Architect
ure
Enterprise
Architectu
re
Enterpris
e
Architect
ure
Enterpris
e
Architect
ure
Domain
Public
and
commer
cial
Comme
rcial
Enterpri
se
Public
Military
Public
Public
Public
Implementation language
Prolog
Ontolin
gua
(Base on
KIF)
At the
concept
ual level
and
UML
At the
conceptua
l level and
UML
At the
conceptual
level and
UML
has
provided
its own
modeling
language
At the
conceptu
al level
and UML
Representation
Heavyw
eight
Heavyw
eight
lightwei
ght
lightweig
ht
lightweigh
t
lightweig
ht
lightweig
ht
Ontology content and
components
Organiz
ation,
Resourc
e,
Organiz
ation’s
Activity
,
Purpose
area,
Actor
area,
Activity,
Capabilit
y,
Resource
Governanc
e,
Service,
Process,
3 layers
of
business,
applicati
Taxonom
y
Structure
Connecti

17
TOVE
The
Enterpr
ise
Ontolog
y(TEO)
Context
-based
DODAF
Data
Meta
Model
TOGAF
Content
Model
ArchiM
ate
UAF
[68]
[37]
[40]
[41]
[64],
[63]
[45],
[65]
[66],
[67]
Activity
, time,
cost
Strategy
,
Marketi
ng,
Time
Action
area,
Object
area,
Facility
area,
LOcatio
n area,
Time
area
(Informati
on,
Performer
and
Material),
Location,
Guide
Data,
Infrastruct
ure,
Motivatio
n
on and
technolo
gy that
stand
under
each
concepts.
vity
Processes
States
Interactio
n
Scenarios
Informati
on
Constrain
ts
Roadmap
Traceabil
ity
Ontology
maturity
and
evaluation
Reusable
Mid
Mid
High
Mid
Mid
Mid
Mid
Accuracy
Low
Low
Mid
Mid
High
High
Mid
Expandabili
ty
Low
Low
High
High
High
High
High
Adaptability
Low
Low
Low
Mid
High
High
High
Completene
ss
Low
Low
Mid
High
High
High
High
3-3- Investigating the Ontology of Enterprise Completeness Using the
Zachman Framework
Enterprise ontology studies examine various dimensions of organizations. For instance,
Leppanen introduced a context-based ontology[40] that encompasses seven dimensions:
goal, actor, action, object, facility, location, and time. The TOVE project[68], [39]
considered four dimensions: ontology of organization[68], ontology of resource[69],
ontology of activity[70], and ontology of cost[71]. ArchiMate[65] presented a meta-
model structured into three layers: business, application, and technology. Additionally,
ArchiMate categorizes its elements into three groups: active elements that perform
actions, behavioral elements that represent the behavior of active elements, and passive
elements that are acted upon by behavioral elements. The TOGAF Content Model[64],
[63] outlines concepts such as motivation, infrastructure, data, process, service, and
governance at the first level.
The Zachman Framework[72], [73] aims to examine all dimensions of an organization,
making it a suitable foundation for studying enterprise ontology models. The columns of
the Zachman Framework represent various aspects (dimensions) of an organization,
derived from the 5W1H questions: who (responsibilities), when (time), why (motivation),

18
where (location), how (task), and what (data). A central question arises: does the ontology
cover all dimensions of the enterprise? If it does, then the enterprise ontology will
demonstrate good comprehensiveness. The six communication questions of 5W1H help
clarify the dimensions of organizations, as noted by Caetano et al. [74] and Zachman.
Rajabi et al. [17] present an enterprise ontology model based on the factors in the columns
of the Zachman Framework. By utilizing the 5W1H questions, Zachman clarifies each
dimension of the organization, providing a solid basis for understanding existing
ontologies and their covered dimensions. In this paper, we will compare the completeness
of enterprise ontology models using the columns of the Zachman Framework.
In addition to reviewing enterprise ontology concepts (see Table 3), we also assess their
compatibility with the Zachman Framework. The selected ontologies for comparison are
leading models that have sufficient documentation available. For instance, the TOGAF
Content Meta-Model introduces important concepts such as data entity, value stream,
constraint, role, organization unit, location, business service, process, function, and
business capability. Each of these concepts is comprehensively defined along with their
relationships to one another. Within the Zachman Framework, the concepts can be
categorized as follows: data entity and value stream fall under the "What" column;
process, function, and business service are placed in the "How" column; location is
categorized under "Where"; and organization unit is found in the "Who" column. Notably,
there are no concepts represented under the "When" column. As illustrated in Table 3, the
Context-Based Enterprise Ontology defines specific areas for each column of the
Zachman Framework and outlines various concepts for each area. This model
demonstrates better alignment with the columns of the Zachman Framework, enhancing
its adaptability and relevance.

19
Some ontology models include "business product" as a key concept within enterprise
ontology. For instance, ArchiMate[65],[65] considers "product" as an essential element
of the organization, defining it as anything offered to the outside world. This definition
also encompasses products that may be provided internally to different parts of the
organization. Thus, the concept of "product" is crucial, yet it is overlooked in some other
ontologies.
The concept of "location" is included in the ontology of organizations, but some models,
such as DODAF, TOGAF, and ArchiMate, limit their definition to a single concept of
"location." In contrast, the Context-Based Enterprise Ontology and the Unified
Architecture Framework (UAF) consider multiple concepts related to location. On the
other hand, both TOVE and enterprise ontology do not address any concepts for the
"where" column. The concept of "business service" in TOGAF[64] supports business
capabilities through an explicitly defined interface and is governed by an organization.
Similarly, the UAF defines "service specification" as a set of functionalities provided by
one element for use by others. This indicates that the concept of "service" is significant
within enterprise architecture; however, it is absent from models that focus solely on
organizational modeling. Table 4 summarizes these findings regarding adaptability
within the Zachman Framework, while Table 5 presents the final results for the
adaptability of existing models according to the Zachman Framework.

20
Table 4: A Review of the Adaptability of Existing Concepts in Enterprise Ontology Within the Zachman Framework
Enterprise ontology
Why
What
How
Who
Where
When
TOVE[68]
Goal, Sub goal
Activity, Constraint, Authority
Communication link
Resource, Organization, Division,
Subdivision, Team, Agent, Role,
Skill
The Enterprise
ontology [37]
Purpose, Mission
CSF, Objective,
Vision, Goal
Activity, Activity Spec, Sub-
Activity, Execute, Plan, Sub-
Plan, Process Spec, Org.
Structure, Strategy, Risk,
Capability
Entity, Role, Relation Attribute
Resource, Person, Corporation,
Unit, Actor, Machine, Actor Role,
Skill, Activity Owner, Doer,
Authority
Time point, Time Interval,
T-Begin, T-End, Time
Line, Calendar, Date,
Duration
Context-based
Enterprise Ontology
[40]
Reason, Purpose
Goal
Function, Activity, Task,
Action structure
Facility, Resource, Tool, Human
actor, Person, Group, Position,
role, Unit, Organization
Location Area, Physical
location Point , Spatial thing
Logical, location
Region, Geographical
dimension, Geographical
system
Time, Time point,
Time interval, Time unit,
Time system, Clock time,
Calendar time
DODAF Data Meta
Model [41]
Vision, Desired Effect
Project, Capability, Activity,
Guidance, Condition
Personnel Type, Skill, Performer,
Data, Information, Materiel
Location
TOGAF Content
Meta Model
[64], [63]
Business Service
Function, Process, Value
Stream, Course of Action,
Business Capability,
Constraint
Organization Unit, Function, Role,
Data Entity
Location
ArchiMate[45],[65]
Business service,
Business product
Business process, Business
function, Business interaction,
Business event, Contract
Business Role, Business actor,
Business collaboration, Business
object,
Location, Business interface
UAF
[66],[67]
Service, Service
Specification,
EnterpriseVision,
EnterpriseGoal
Capability, Project Kind,
Project Activity, Project
Milestone, Capable Element
Project, Actual Milestone
Kind, Operational Activity
Actual Organization,
Organizational Resource, Person,
Post, Responsibility, Natural
Resource, Physical Resource,
Resource Architecture, Resource
Artifact, Resource Performer,
Software System, Standard,
Protocol, Protocol Stack
Location, Location Holder,
Location Kind, Actual
Location

21
Table 5: Results of the Adaptability of Existing Concepts in Enterprise Ontology Within the Zachman Framework
Conceptual Model
TOVE
The Enterprise
ontology(TEO)
Context-
based
DODAF
Data Meta
Model
TOGAF
Content
model
ArchiMate
UAF
Adaptability with
the Zachman
framework
×
×
✓
×
×
×
×
4- Results analysis
Numerous studies have explored enterprise ontology; however, many exhibit significant
weaknesses. A critical issue is the lack of consensus on which dimensions of an organization
should be included in its ontological model. For instance, while one study[68] incorporates the
time dimension, another study[41] overlooks it entirely. Establishing a common agreement on
the conceptual model is essential before progressing to formal and logical construction phases.
We require a foundational conceptual model that accurately represents key concepts and
relationships, yet currently, there are no standard or reference models available for researchers
to consult.
The existing enterprise ontology models often lack conceptual depth, leading to premature
transitions into the implementation phase. This results in underdeveloped conceptual models,
making their formal counterparts neither reusable nor expandable. Furthermore,
comprehending formal models becomes challenging without a robust conceptual framework.
Additionally, the concepts present in current ontologies do not adequately encompass all
components of an organization. If ontological models were better aligned with the columns
outlined in Zachman’s framework, they could more effectively cover various organizational
dimensions. There is an urgent need for a powerful conceptual model that articulates
fundamental concepts and relationships in an interpretable manner for diverse applications,
including enterprise architecture, business architecture, business process management, context-
aware systems, intercommunication, and automated production and analysis of models.

22
Moreover, there is no standardized approach to expand and customize a generic enterprise
ontology model for specific domains or organizational needs.
Conceptual models of enterprises can be enriched progressively: initially, a general enterprise
ontology model is developed; then it is refined for specific industries; ultimately, an enterprise-
specific ontology emerges based on previous models. This progression is illustrated in Figure
2. In general, all organizations share a set of common principles and concepts that form their
foundation. In the second stage, these common concepts are detailed to suit various types of
organizations—such as commercial entities, military organizations, and universities. Finally,
in the third step, appropriate common concepts are tailored specifically to describe individual
organizations
Figure 2: The Relationship Between Enterprise Ontology and Its Subsets (Development and Expansion of
Ontology)
5- Conclusion
Enterprise ontology offers a comprehensive and systematic framework that enhances the
understanding of organizations for both managers and stakeholders. By addressing ambiguities
and contradictions, this framework empowers informed decision-making. The clarity it
provides is invaluable not only for human users but also for machine processing. This
structured understanding is applicable across various domains, including efficient modeling of
enterprise architecture, business processes, and context-aware systems. In this study, we
Expanded
organizational ontology
for the unique enterprise
Domain-specific
enterprise ontology
Public enterprise
ontology

23
examined and compared conceptual models of enterprise ontology, highlighting their strengths
and weaknesses. Future research should focus on developing a reference enterprise ontology
model that encompasses all dimensions of an organization, ensuring it covers every component
while identifying key elements and relationships. Additionally, there is a critical need to
establish reliable methods for adapting the reference enterprise ontology model to create
tailored models for specific domains, organizations, or applications.
Conflicts of Interests
The authors did not receive support from any organization for the submitted work.
Author Contributions Statement
Seyed Mohsen Rahnamafard conceptualized the research idea, laying the groundwork for the
study. Zeinab Rajabi further developed this concept, refining the initial ideas and enhancing
the framework for analysis. Both authors, collaborated closely in conducting the analysis and
comparisons presented in this study, ensuring a comprehensive evaluation of the existing
literature on enterprise ontology.
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