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
,
Jo
˜
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
1
Military Institute of Engineering, Rio de Janeiro, RJ, Brazil
2
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
a
https://orcid.org/0000-0002-0533-3395
b
https://orcid.org/0000-0002-0240-4506
c
https://orcid.org/0000-0002-1153-3879
d
https://orcid.org/0000-0003-1418-0658
e
https://orcid.org/0000-0003-4030-7149
f
https://orcid.org/0009-0001-7032-5850
g
https://orcid.org/0000-0003-4655-8889
h
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
Copyright
c
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
1
. 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
1
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
2
is the most expressive language.
It incorporates formal semantics that comes from the
OWL Web Ontology Semantics and Abstract Syntax
(OWL S&AS)
3
. 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)
4
. 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
2
https://www.w3.org/OWL/
3
https://www.w3.org/TR/owl-semantics/
4
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)
5
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.
5
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
6
.
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
6
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
7
for VisualParadigm
8
. and is available in the Gitlab repository of the
7
https://github.com/OntoUML/ontouml-vp-plugin
8
https://www.visual-paradigm.com/download/commu
nity.jsp
project
9
. 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
´
eg
´
e
10
, and it was possible to specialize and
instantiate the ontology elements. The scenario de-
veloped using ontology analysis is exported from
Prot
´
eg
´
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
11
. 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>
<Class IRI="#CommDeviceCarrier"/>
<NamedIndividual IRI="#22"/>
</ClassAssertion>
<ClassAssertion>
<Class IRI="#CommDeviceOperator"/>
<NamedIndividual IRI="#22"/>
</ClassAssertion>
<ClassAssertion>
<Class IRI="#MilitaryPerson"/>
<NamedIndividual IRI="#22"/>
</ClassAssertion>
<ClassAssertion>
<Class IRI="#MilitaryPersonAsCarrier"/>
<NamedIndividual IRI="#22"/>
</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
9
https://gitlab.com/andredemori/ontology-based-
scenario-repository
10
https://protege.stanford.edu/
11
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>
<ObjectProperty IRI="#isLocatedIn"/>
<Variable IRI="#m1"/>
<Variable IRI="#mp"/>
</ObjectPropertyAtom>
<ClassAtom>
<Class IRI="#MilitaryPerson"/>
<Variable IRI="#m1"/>
</ClassAtom>
<ClassAtom>
<Class IRI="#MilitaryPlatform"/>
<Variable IRI="#mp"/>
</ClassAtom>
<ObjectPropertyAtom>
<ObjectProperty IRI="#belongsTo"/>
<Variable IRI="#mp"/>
A Semantic Web Approach for Military Operation Scenarios Development for Simulation
395
<Variable IRI="#mo"/>
</ObjectPropertyAtom>
<ClassAtom>
<Class IRI="#MilitaryOrganization"/>
<Variable IRI="#mo"/>
</ClassAtom>
</Body>
<Head>
<ObjectPropertyAtom>
<ObjectProperty IRI="#
militaryHasMilitaryOrganization"/>
<Variable IRI="#m1"/>
<Variable IRI="#mo"/>
</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>
<ObjectProperty IRI="#mayTalkTo"/>
<NamedIndividual IRI="#sta5"/>
<NamedIndividual IRI="#sta14"/>
</ObjectPropertyAssertion>
<ObjectPropertyAssertion>
<ObjectProperty IRI="#mayTalkTo"/>
<NamedIndividual IRI="#sta5"/>
<NamedIndividual IRI="#sta15"/>
</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>
<Class IRI="#Guarani"/>
<Variable IRI="#g"/>
</ClassAtom>
</Body>
<Head>
<DataPropertyAtom>
<DataProperty IRI="#AmphibiousSpeed"/>
<Variable IRI="#g"/>
<Literal datatypeIRI="http://www.w3.org
/2001/XMLSchema#integer">9</Literal
>
</DataPropertyAtom>
<DataPropertyAtom>
<DataProperty IRI="#MaxSpeedLand"/>
<Variable IRI="#g"/>
<Literal datatypeIRI="http://www.w3.org
/2001/XMLSchema#integer">95</
Literal>
</DataPropertyAtom>
<DataPropertyAtom>
<DataProperty IRI="#MinSpeed"/>
<Variable IRI="#g"/>
<Literal datatypeIRI="http://www.w3.org
/2001/XMLSchema#decimal">3.5</
Literal>
</DataPropertyAtom>
<DataPropertyAtom>
<DataProperty IRI="#VisibilityRange"/>
<Variable IRI="#g"/>
<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
ˆ
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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