A Semantic Web Approach for Military Operation Scenarios

Development for Simulation

Andr

e M. Demori

1 a

, Julio Cesar Cardoso Tesolin

1 b

, David Fernandes Cruz Moura

1 c

ao Eduardo Costa Gomes

2 d

, Gabriel Pedroso

2 e

, Leonardo Filipe Batista Silva de Carvalho

2 f

Edison Pignaton de Freitas

2 g

and Maria Cl

audia Reis Cavalcanti

1 h

Military Institute of Engineering, Rio de Janeiro, RJ, Brazil

Federal University of Rio Grande do Sul, Porto Alegre, Brazil

Keywords:

Semantic Web, Domain ontologies, Decision-making, Modeling and Simulation.

Abstract:

The reality simulation process by computational means allows decision-makers to analyze and propose the

best strategies to be adopted in a real environment. However, the scenario, sometimes heterogeneous, as in

the case of military operations, requires formalization to achieve domain knowledge, allowing a more faith-

ful reproduction of reality. In the case of military operation scenarios that address tactical, operational, and

strategic elements and the use of communications, formalization could help organize knowledge, data shar-

ing, and decision-making. This article proposes (i) the use of conceptual modeling that is based on concepts

arising from a foundation ontology named UFO (Uniﬁed Foundational Ontology), (ii) the use of Web On-

tology language (OWL), and (iii) the use of rule deﬁnitions expressed in the Semantic Web Rule Language

(SWRL). Through this approach, this article describes the process of formalizing the domain knowledge as a

reference and its corresponding operational ontology by identifying entities, relationships, rules, and all the

categorizations made in the ontology for execution and decision-making in a battleﬁeld simulator that is still in

production. The application of this ontology is illustrated in representative and real-world examples, showing

promising results of the proposed approach.

1 INTRODUCTION

During the planning process of a military operation,

an examination of its operational elements must be

done to understand how they would behave and in-

ﬂuence the decision-making procedure. The hetero-

geneous wireless communication system is essential

to the military operation scenario. Here, the hetero-

geneity is not only related to the existence of different

radio systems on different channels but also to differ-

ent military units, air and ground platforms, variables

such as distance, terrain, weather, troop mobility, high

https://orcid.org/0000-0002-0533-3395

https://orcid.org/0000-0002-0240-4506

https://orcid.org/0000-0002-1153-3879

https://orcid.org/0000-0003-1418-0658

https://orcid.org/0000-0003-4030-7149

https://orcid.org/0009-0001-7032-5850

https://orcid.org/0000-0003-4655-8889

https://orcid.org/0000-0003-4965-9941

demand for information transfer rate, among others

(Marcus et al., 2018).

The reproduction of reality through simulation al-

lows decision-makers to verify alternatives outside

the real environment, providing statistical analysis

and the necessary data to deal with each possible sce-

nario. However, the representation and reproduction

of a wireless communication military scenario is a

challenge once it deals with variables not only lim-

ited to the characterization of the wireless communi-

cations systems. It also deals with variables related to

the environment where the wireless communication

system is used.

The Semantic Web provides representation and

reasoning resources, such as ontologies and formal

languages, that are useful to represent and simulate

those military communication scenarios. Neverthe-

less, for a machine to make inferences and interpret

the existing content in a dataset shared by different

agents, there must be a formal model of a portion of

reality that allows it to understand, reason, and make

390

Demori, A., Tesolin, J., Moura, D., Gomes, J., Pedroso, G., Silva de Carvalho, L., Pignaton de Freitas, E. and Cavalcanti, M.

A Semantic Web Approach for Military Operation Scenarios Development for Simulation.

DOI: 10.5220/0012088600003541

In Proceedings of the 12th International Conference on Data Science, Technology and Applications (DATA 2023), pages 390-397

ISBN: 978-989-758-664-4; ISSN: 2184-285X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

decisions. In computing, building such formal mod-

els is the exercise of developing ontologies, which can

help solve problems of semantic ambiguity, interoper-

ability, and formalization of knowledge through tax-

onomies and rules.

The W3C Web Ontology Language (OWL) is a

Semantic Web language designed to represent rich

and complex knowledge about things

. However, pre-

vious to expressing ontologies in it, it is necessary

to elicit requirements and identify concepts and re-

lationships for a given domain. Conceptual mod-

elling languages are essential, as are ontology devel-

opment methodologies, to guide domain modellers in

this task.

Although there are some tools that allow the sim-

ulation of communication networks, as far as it was

investigated, none of them is able to deal with the

demands of military scenario representation. On the

other hand, there are works (Tzeng et al., 2009)

(Gilmour et al., 2006) that propose ontologies to rep-

resent such scenarios. However, none of them follow

the steps of an ontology development methodology,

generating a well-founded operational ontology.

Therefore, this work presents an ontology to rep-

resent the communication environment in military

tactical scenarios. This ontology aims to achieve a

signiﬁcant portion of the domain knowledge for these

scenarios, allowing semantic reasoning to assist in

decision-making in the simulation process. We also

present the ﬁrst results of an operational scenario

planning process.

This document is structured as follows. Section 2

introduces the basic concepts used in this paper, while

Section 3 discusses some related works. Section 4

describes the typical scenario of a military operation.

Based on this scenario a reference ontology and its

implementation were developed and are presented in

Sections 5 and 6, respectively. Section 7 concludes

and offers perspectives for future work.

2 BACKGROUND

Although the Semantic Web encourages the design

of ontologies to represent and reason over data from

any domain, it is strongly recommended to follow

an ontology development methodology. Among the

ones available in the literature, we chose to use

SABiO (Systematic Approach for Building Ontolo-

gies) (de Almeida Falbo, 2014), which distinguishes

between two types of domain ontologies: reference

and operational. A reference ontology is a domain

https://www.w3.org/OWL/

ontology that is a solution-independent speciﬁcation

(conceptual model) built with the goal of making the

best possible description of the domain. Accord-

ing to SABiO development process, once a reference

ontology is obtained, operational versions (machine-

readable ontologies) of it can be implemented.

Moreover, SABiO recognizes the importance of

the use of foundation ontologies in the development

of reference ontologies and proposes applying onto-

logical analysis during the ontology design. In this

analysis, the relevant concepts and relations should

be identiﬁed and organized according to highly-

expressive languages, such as OntoUML(Guerson

et al., 2015). It incorporates important foundational

distinctions, which enables the designer to create

strongly axiomatized domain ontologies that can re-

ﬂect the domain, as closely and adequately as pos-

sible, and serve as a reference. Those distinc-

tions come from the Uniﬁed Foundational Ontology

(UFO), which is described in Section 2.1.

A well-founded reference ontology may produce

and implement the best possible corresponding oper-

ational ontology. However, it is worth saying that just

as a reference ontology is a simpliﬁed representation

of reality, similarly, an operational ontology usually

implies a simpliﬁcation of a reference ontology. Sec-

tion 2.2 describes brieﬂy the Semantic Web and some

of its proposed languages, which are suitable to rep-

resent operational ontologies.

2.1 UFO

The use of foundation or upper ontologies in con-

ceptual modeling attempts to produce richer models,

favoring a faithful representation as close as possi-

ble to the real world. The Uniﬁed Foundational On-

tology (UFO) presented by Guizzardi (2005) is one

of the prominent upper ontologies currently available

that has been used to face the challenge of building

well-founded and formalized conceptual models in

different domains (Griffo et al., 2016) (Barcelos et al.,

2016) (Fabio et al., 2021). It is divided into three

fragments for better understanding: UFO-A, UFO-B,

and UFO-C. UFO-A is also known as the Ontology

of Endurants and deals with modeling types whose

instances are object-like entities, having their founda-

tions in two principles: Existential Dependency and

Identity. On the other hand, UFO-B, or the Ontology

of Perdurants, deals with entities bounded in time, in

contraposition with Endurants, which have their iden-

tities preserved over time, even if their essential or

contingent properties may change. Finally, UFO-C,

or the Ontology of Social and Intentional Entities, is

built upon UFO-A and UFO-B and represents social

A Semantic Web Approach for Military Operation Scenarios Development for Simulation

391

and intentional aspects (Guizzardi et al., 2008).

According to SABiO, to capture and formalize a

domain ontology, the modeler should classify each

concept according to the categories deﬁned in UFO

(kind, subkind, phase, role, category, rolemixin etc.).

Moreover, the constraints for relating these types

should be respected in order to achieve consistent

conceptual models. As mentioned before, these types

and their corresponding constraints and ontological

patterns are incorporated into the OntoUML.

2.2 Semantic Web

As deﬁned by Berners-Lee et al. (2001), the Semantic

Web is an extension of the current web in which in-

formation is given a well-deﬁned meaning, allowing

for greater cooperation between computers and peo-

ple. As such, machines can understand data rather

than display it, becoming not only a document stor-

age network but also a means of automatic informa-

tion processing. Nevertheless, importing, represent-

ing, and linking data with information from different

ﬁelds of knowledge is a challenge.

For machines to be capable of understanding data

and of automated reasoning, a set of languages and

standards has been proposed by the W3C, such as

XML, RDF, RDFS, SPARQL, OWL, SWRL, and

SKOS. The OWL

is the most expressive language.

It incorporates formal semantics that comes from the

OWL Web Ontology Semantics and Abstract Syntax

(OWL S&AS)

. It is based on computational logic,

so a computer program can check the consistency of

knowledge expressed in OWL or make explicit the

implicit knowledge. OWL also allows representing

the rule axioms generated by SWRL (Semantic Web

Rule Language)

. SWRL can be used to create con-

straints on deﬁning relationships and instances and to

assign attribute values to the instance of a class in on-

tology using rules that consist of an antecedent and a

consequent.

3 RELATED WORKS

Tzeng et al. (2009) present the Ontology-based Mil-

itary Scenario Resources Management (OMSRM)

platform to demonstrate how ontology technologies

can be adopted to improve the reuse capabilities of

military scenario resources. The paper presents a

study that uses OWL to create a multi-layered ontol-

ogy architecture that provides RDF-based metadata

https://www.w3.org/OWL/

https://www.w3.org/TR/owl-semantics/

https://www.w3.org/Submission/SWRL/

and OWL-based ontologies for the OMSRM plat-

form. However, the creation of the ontology was

not supported by a foundation ontology, which would

help to distinguish concepts and enrich the semantics.

For example, according to their model, a military unit

may use some equipment that could be a vehicle or a

communication device. It misses the notion that the

vehicle ”carries” a device, which would inform how

fast that device is moving. This is important for sim-

ulating a communications scenario in military opera-

tions.

In turn, Gilmour et al. (2006) presents the SGen

aimed at simulations for the military scenario do-

main. SGen can generate simulation scenarios and

run them in parallel using high-performance comput-

ing (HPC) to evaluate alternatives for war plans by

simulating large numbers of data sets. SGen also

uses an ontology-based approach through ontology

formalization, reasoning, and inference capabilities to

create a meta-model representing a domain of inter-

est, in this case, military mission planning. However,

they do not report on the use of a foundation ontol-

ogy, nor do they provide a detailed overview of the

developed ontology.

The Simulation Interoperability Standards Orga-

nization (SISO)

focuses on facilitating interoperabil-

ity between simulation systems and reuse through

several standards. Nevertheless, as pointed out by

Tzeng et al. (2009), these standards do not address

how ontologies can be a way to represent knowledge

to facilitate machine reasoning and facilitate the reuse

of resources from the military scenario.

Some representation challenges in the military

scenario domain are indicated by Lacy and Gerber

(2004), such as the representation of behaviors, de-

scriptions of units and objects to be simulated as mil-

itary units, descriptions of entities (e.g., military plat-

forms), descriptions of entity attributes, and scenar-

ios. The authors point out that through the consis-

tent OWL syntax, it is possible to have better infor-

mation sharing, support for reasoning through infer-

ence systems, and support for the modeling and sim-

ulation community for the representation challenges

cited above. Despite this argument for the use of

OWL, they do not actually present an ontology for

military scenarios.

In turn, the work presented by Tesolin et al. (2020)

proposes the modeling of an ontology using UFO in

the context of wireless networks, more speciﬁcally, in

the domain of critical communications networks. The

result is a clearer and unambiguous speciﬁcation of

the situations within the scenario, thus assisting in the

decision-making process through semantic reasoning.

https://www.sisostds.org/

DATA 2023 - 12th International Conference on Data Science, Technology and Applications

392

Although this work does not address modeling for a

simulation-oriented scenario, it has a great similar-

ity with the methodology adopted for reasoning and

decision-making of the present work.

Finally, the present work continues what was pre-

sented by Demori et al. (2022), showing an attempt

at modeling using UFO for a military communication

scenario. The initial modeling of an ontology formal-

ized and helped elaborate the simulation of a tacti-

cal environment. However, neither a serialization of

the scenario in OWL, nor the representation of rules

in SWRL, was performed to make processing by the

simulator possible. Besides, the ontology presented

in this work still contemplated a few elements of the

military scenario.

4 THE MILITARY OPERATION

SCENARIO

During the planning of complex and heterogeneous

scenarios such as those found in a military opera-

tion for simulation applications, ontological analysis

can be used for decision support, knowledge man-

agement, a better semantic deﬁnition for ambiguous

terms present in glossaries and manuals, information

sharing, and achieving a shared conceptualization,

among others.

The preliminary process of ontology develop-

ment, developed from a conceptual modeling method-

ology, was the research about which elements should

be part of the scenario in the reference ontology. The

research began with surveys of army manuals, arti-

cles, and glossaries related to the topic and meet-

ings with experts. As a result, the elements that

compose the scenario mainly affect the communica-

tion system about node mobility, which involves plat-

forms to move in different terrains, communication

restrictions, device conﬁguration, device operators,

and their respective military organizations. The sce-

nario developed with the ontology analysis in this pa-

per is a continuation of the results shown by Demori

et al. (2022).

Chapter 5 of the Brazilian Army’s military em-

ployment manual C11-1 - Employment of Commu-

nications (EME, 1997) (Emprego das Comunicac¸

oes

in portuguese) addresses several issues regarding the

planning and control of communications. It is demon-

strated that a communication network built in a sce-

nario such as a military operation suffers more signif-

icant interference from the external environment than

a conventional network.

Still on this subject, Bispo et al. (2014) present a

scenario-based analysis supporting military commu-

nications system design. The paper reinforces the

requirements described in the previously mentioned

Communications Employment Manual, providing a

more detailed description of the operational scenario

variables, as given by domain experts. In the afore-

mentioned work, some concepts were based on op-

erational scenario variables, such as participants, mo-

bility, and platform, as well as on communication sce-

nario variables, such as range and frequency, among

others described in the article.

Other information sources for the development of

the scenario were taken from the Brazilian Army’s

digital library

5 REFERENCE ONTOLOGY FOR

MILITARY OPERATION

SCENARIOS

This Section presents a reference ontology of a sce-

nario for further simulation in an environment in-

volving a network emulator. It covers elements from

the military domain and some elements from the net-

working domain, and it was created using an imple-

mentation of the OntoUML language (Guerson et al.,

2015).

In the case of elements from the military do-

main, for example, in Figure 1, military organi-

zations (MilitaryOrganization), as well as their

types (MilitaryOrganizationPowerType) are rep-

resented. Moreover, military organizations can have

different roles concerning other military organiza-

tions, such as subordination role (Subordinate) and

commander role (Commander).

It is usual that the communication between

members of two organizations is restricted according

to their hierarchical roles. For example, when two

organizations are in hierarchically equivalents roles

(HierarchicallyEquivalentDuringCommunica-

tion), or when one of them is directly subordinated

to the other, they are allowed to communicate.

Otherwise, they are not allowed to. Thus, decisions

can be made to restrict communications according

to the military organization hierarchy levels, and

this should be reﬂected in the network emulation

environment.

In turn, a person (Person) can be specialized

in a military person (MilitaryPerson) when

it belongs to a military organization for an em-

ployment relationship (MilitaryEmployment).

Also, a military person operating a commu-

nication device (CommDevice) is an operator

https://bdex.eb.mil.br/jspui/

A Semantic Web Approach for Military Operation Scenarios Development for Simulation

393

(CommDeviceOperator), and those located in a vehi-

cle are passengers (MilitaryPersonAsPassenger).

However, a military can also carry a communica-

tion device on foot (MilitaryPersonAsCarrier).

Whether a military person operating a device is on

foot or in a vehicle will directly affect the speed

at which a communication device moves in the

network emulation. So there are the elements that are

characterized by carrying a communication device

with them (CommDeviceCarrier). They are either a

military person or a platform (MilitaryPlatform).

Military vehicles (Vehicle) are part of the platform

category. In turn, armored vehicles (Armored) are

part of the vehicle category, and a Guarani (Guarani)

is a type of armored vehicle.

In addition to the communication device

(CommDevice), other elements are present to

represent the network environment, such as the

wireless network (WirelessNetwork) and the access

points (AccessPoint). A communication device has

interfaces (Interface) that must be conﬁgured in

the network emulator. These interfaces also have a

type InterfacePowerType with speciﬁc attributes.

Some classes shown in the modeling have rel-

evant attributes for the scenario analysis. For

example, the CommDeviceCarrier class has at-

tributes referring to locomotion speed (MinSpeed,

MaxSpeedLand) and ﬁre range (VisibilityRange)

for experiments related to fratricides. Likewise,

the Guarani class has an attribute referring to its

amphibious speed (AmphibiousSpeed) since this

type of vehicle can travel in water. In turn,

the CommDevice class has attributes such as range

(Range) and antenna gain (AntennaGain). Finally,

each interface type has attributes such as the data

transfer method (DataTransferMethod), the fre-

quency (Frequency), and the power level transmis-

sion (Txpower).

All elements represented in the modeling were

categorized following the UFO taxonomy, especially

the UFO-A fragment that addresses issues such as

identity principle, existential dependency, and rigid-

ity. The approach adopted in this work helped formal-

ize the scenario development using ontological anal-

ysis, bringing a semantic enrichment. Given this sce-

nario, the foundation ontology optimizes cognitive ca-

pacity in deﬁning roles, phases, all-part relationships,

rules, and responsibilities.

The conceptual modeling with all the scenario

elements for the battleﬁeld simulation was devel-

oped with the OntoUML plugin

for VisualParadigm

. and is available in the Gitlab repository of the

https://github.com/OntoUML/ontouml-vp-plugin

https://www.visual-paradigm.com/download/commu

nity.jsp

project

. Figure 1 shows the conceptual modeling de-

veloped.

6 IMPLEMENTATION

The operational ontology was implemented in OWL

based on the reference ontology presented in Sec-

tion 5. It was generated through a lightweight im-

plementation of the UFO, the gUFO (Almeida et al.,

2020). The OWL version of the ontology was loaded

into Prot

, and it was possible to specialize and

instantiate the ontology elements. The scenario de-

veloped using ontology analysis is exported from

Prot

e and documented in OWL format, allowing

the rich representation of knowledge that includes en-

tities, individuals, categories, inferences, attributes,

rules, and relationships. This Section shows how data

and information from the military scenario are docu-

mented and processed through OWL. Listing 1 shows

an OWL/XML Syntax code fragment of an ontology

where the ClassAssertion axioms of a given individ-

ual are being represented. The ClassAssertion axiom

allows stating that an individual is an instance of a

particular class

. In this case, the individual 22 is

instantiated as an instance of class MilitaryPerson,

and by reasoner inference on account of rules or in-

heritance relationships, it is also instantiated in other

classes.

</ClassAssertion>

</ClassAssertion>

</ClassAssertion>

</ClassAssertion>

Listing 1: Class Assertion example.

Through the combination of SWRL rules and car-

dinality constraints, restrictions were deﬁned in the

ontology for instantiations and relationships. The

reasoner must indicate an ontological error if these

https://gitlab.com/andredemori/ontology-based-

scenario-repository

https://protege.stanford.edu/

https://www.w3.org/TR/owl2-syntax/

DATA 2023 - 12th International Conference on Data Science, Technology and Applications

394

Figure 1: Conceptual modeling of military operation scenario.

restrictions are not obeyed. For example, when

creating an instance of the class MilitaryPerson,

it must be related to an instance of the class

MilitaryOrganization to which that military per-

son belongs. The same is true for instances of

MilitaryPlatform. Therefore, when creating a rela-

tionship between a military person and a platform, es-

tablishing that the military person is inserted in a spe-

ciﬁc vehicle, for example, a constraint says that the

military person must belong to the same military or-

ganization as the vehicle. Otherwise, an error will be

indicated. This was done by establishing a cardinality

constraint between the military person and the organi-

zation. Since the military person can only be associ-

ated with at most one organization, so using SWRL, if

an instance of a military person has a relationship with

a platform (isLocatedIn), then by consequence, that

military person belongs to the same organization as

the platform. Since the military organization to which

the military person belongs had already been deﬁned

when the military person instance was created, it will

cause a logical error if the military person’s organiza-

tion is different from the platform’s organization. The

SWRL rule presented in equation 1, represents this

constraint.

MilitaryPlat f orm(?mp)∧

MilitaryOrganization(?mo)∧

belongsTo(?mp, ?mo)∧

MilitaryPerson(?m1)∧

isLocatedIn(?m1, ?mp) →

militaryHasMilitaryOrganization(?m1, ?mo)

(1)

The code fragment presented in Listing 2 shows

how the ontology information expressed in the OWL

ﬁle inside the DLSafeRule marker is used to allow

rule creation.

<Body>

</ObjectPropertyAtom>

</ClassAtom>

</ClassAtom>

A Semantic Web Approach for Military Operation Scenarios Development for Simulation

395

</ObjectPropertyAtom>

</ClassAtom>

</Body>

<Head>

<ObjectProperty IRI="#

militaryHasMilitaryOrganization"/>

</ObjectPropertyAtom>

</Head>

Listing 2: Rule axiom example.

In this sense, a set of rules was created to de-

ﬁne restrictions on communication between devices

during the simulation process since the communica-

tion devices can only communicate with others if the

mayTalkTo relationship represented in de modeling in

Figure 1 exists between them. For instance, the sim-

ulation may have to follow two rules: (i) Two devices

can communicate if there is a relationship of subordi-

nation between the military organizations of the mil-

itary persons operating the devices; (ii) Two devices

can communicate if the military persons operating the

devices belong to the same military organizations..

Depending on the scenario the rules can be changed.

The code fragment presented in Listing 3 shows

how these rules are expressed in OWL. In this exam-

ple, the communication device sta5 has a relationship

mayTalkTo with sta14 and sta15 communication de-

vices, allowing the communication between them.

</ObjectPropertyAssertion>

</ObjectPropertyAssertion>

Listing 3: Object property assertion examples.

Other SWRL rules are applied to set attribute val-

ues (or data properties) for some entities in our mil-

itary scenario. For instance, the rule in Equation 2

shows how the attributes related to speed and ﬁre

range restrictions are set for the combat vehicle type

Guarani. The code fragment presented in Listing

4 shows the result of the ontological reasoning after

evaluating Equation 2.

Guarani(?g) →

AmphibiousSpeed(?g, 9)∧

MaxSpeedLand(?g, 95)∧

MinSpeed(?g, 3.5)∧

VisibilityRange(?g, 1000)

(2)

<Body>

</ClassAtom>

</Body>

<Head>

<Literal datatypeIRI="http://www.w3.org

/2001/XMLSchema#integer">9</Literal

</DataPropertyAtom>

<Literal datatypeIRI="http://www.w3.org

/2001/XMLSchema#integer">95</

Literal>

</DataPropertyAtom>

<Literal datatypeIRI="http://www.w3.org

/2001/XMLSchema#decimal">3.5</

Literal>

</DataPropertyAtom>

<Literal datatypeIRI="http://www.w3.org

/2001/XMLSchema#integer">1000</

Literal>

</DataPropertyAtom>

</Head>

Listing 4: Data property deﬁnitions examples.

The Listings and SWRL rules present some parts

of how the military scenario is created and repre-

sented using Semantic Web technologies. Through

this, a simulator of the scenario is being developed to

process and generate new information based on this

data. For example, the simulator should be able to

read the ontology in OWL and start creating the sce-

nario to be simulated with all the instances passed for

the military and the network environment.

7 CONCLUSIONS

Many environmental changes can happen during a

military operation that may drive the need to change

DATA 2023 - 12th International Conference on Data Science, Technology and Applications

396

the communication technologies. Therefore, repro-

ducing real military scenarios in a computational en-

vironment can help the planning process and improve

ongoing decision-making. Such reproductions can be

done using battleﬁeld simulators or tactical network

simulators.

This work reinforces the use of Semantic Web

technologies to provide resource sharing, allowing

data to be processed so that decisions can be made

during the simulation using semantic reasoners. It

differs from other related works once it uses a well-

founded conceptual modeling approach to categorize

and relate each element of the proposed scenario. Fur-

thermore, its representation richness provides a se-

mantic advantage as it improves interoperability with

other ontologies focused on military or communica-

tion scenarios.

A battleﬁeld communication simulator is cur-

rently being developed with the proposed ontology as

part of our future works. Our goal is to evaluate its

capacity to not only describe different military opera-

tions but also improve the decision-making process in

the ﬁeld as it can adequately responds to environmen-

tal changes such as the increase of radio frequency in-

terferences. Moreover, this future evaluation intends

to test its extension capability while modeling new el-

ements of the operational scenario, while interoperat-

ing with existing models that describe military opera-

tions and their communications environment.

ACKNOWLEDGEMENTS

This research has been funded by FINEP/DCT/-

FAPEB (ref.: 2904/20 conv

enio 01.20.0272.00) un-

der the “Systems of Command and Control Systems”

project (“Sistemas de Sistemas de Comando e Cont-

role”, in Portuguese).

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