Towards an Ontology-Based Approach for Enhancing Animal Sanitary

Event Management

Felipe Amadori Machado

1 a

, Jonas Bulegon Gassen

1 b

, Matheus Flores

1 c

, Francisco Lopes

2 d

Fernando Groff

2 e

, Alencar Machado

1 f

and Vinicius Maran

1 g

Laboratory of Ubiquitous, Mobile and Applied Computing, Federal University of Santa Maria, Santa Maria, Brazil

Departamento de Defesa Agropecu

aria, Secretaria da Agricultura, Pecu

aria e Desenvolvimento Rural,

Porto Alegre, Brazil

famachado@inf.ufsm.br, jonas.gassen@ufsm.br, {viniciusmaran, matheusfriedhein, alencar.comp}@gmail.com,

Keywords:

Ontology, Sanitary Event Management, Real-Time Event Monitoring, Resource Optimization, Emergency

Management.

Abstract:

This study presents the development and integration of a contextual modeling approach for sanitary events,

speciﬁcally focusing on outbreaks affecting animal populations. A specialized ontology for sanitary events was

developed using Prot

e and integrated into the PDSA-RS platform, which supports animal health regulation

in Brazil. The platform aids in the certiﬁcation of poultry and swine farming in Rio Grande do Sul, ensuring

compliance with Brazilian animal health regulations. The system’s effectiveness was demonstrated during

FMD, avian inﬂuenza, and ND outbreak scenarios, where it signiﬁcantly reduced analysis time and improved

ﬁeld team management through real-time task allocation and monitoring. The system’s usability was evaluated

using the System Usability Scale (SUS), resulting in a score of 75.52, reﬂecting positive feedback from users.

Future developments will focus on reﬁning the ontology and enhancing the embedded rules within the system

to better align with real-world sanitary management processes and improve adaptability to various scenarios.

1 INTRODUCTION

Sanitary events involving animals have signiﬁcant

impacts both locally and globally. Diseases affect-

ing animal populations, such as outbreaks of foot-

and-mouth disease, African swine fever, or avian in-

ﬂuenza, can compromise public health, directly af-

fect food security, and result in billions of dollars in

economic losses. For example, the foot-and-mouth

disease outbreak in Germany in January 2025 un-

derscored the rapid and far-reaching consequences of

such events, including trade restrictions (BBC, 2025).

The ﬁnancial impact extends beyond the direct loss of

animals, encompassing costs related to sanitary con-

trols, trade restrictions, and reduced agricultural pro-

https://orcid.org/0009-0005-8179-1987

https://orcid.org/0000-0001-8384-7132

https://orcid.org/0000-0003-4436-4327

https://orcid.org/0009-0000-1808-3404

https://orcid.org/0000-0002-0532-531X

https://orcid.org/0000-0003-2462-7353

https://orcid.org/0000-0003-1916-8893

ductivity (Rushton, 2009). In countries where the

economy is heavily dependent on livestock, such as

Brazil, the damages can be even more pronounced,

disrupting entire production chains and jeopardizing

exports.

The organization and management of sanitary

events, such as farm inspections, the implementa-

tion of containment measures, and outbreak investi-

gations, play a crucial role in mitigating these im-

pacts. However, these tasks are complex and require

efﬁcient planning, team coordination, real-time data

collection and analysis, and mechanisms to adapt to

dynamic scenarios. Context-aware systems emerge as

a promising solution to these challenges, enabling the

capture, processing, and application of relevant infor-

mation in a timely manner for decision making (Cook

and Das, 2004).

In this context, ontologies become essential tools

for formally representing knowledge related to sani-

tary events. They allow for the modeling of entities

such as farms, inspection teams, vehicles, and ﬁeld

activities, as well as the relationships between these

elements (Gruber, 1993).

630

Machado, F. A., Gassen, J. B., Flores, M., Lopes, F., Groff, F., Machado, A. and Maran, V.

Towards an Ontology-Based Approach for Enhancing Animal Sanitary Event Management.

DOI: 10.5220/0013464300003929

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 27th International Conference on Enterprise Information Systems (ICEIS 2025) - Volume 1, pages 630-641

ISBN: 978-989-758-749-8; ISSN: 2184-4992

This paper focuses on the development of a con-

textual modeling approach for sanitary events (out-

breaks), using ontology as the primary tool for de-

scribing and structuring the context. The main con-

tribution is the creation of a specialized ontology for

sanitary events, developed in Prot

e (v5.6.4), which

provides a structured framework for representing the

complex relationships and data involved in outbreak

management. This ontology was subsequently inte-

grated into the PDSA-RS platform.

The Plataforma de Defesa Sanit

aria Animal do

Rio Grande do Sul (PDSA-RS) is a platform designed

to support animal health regulation in Brazil, integrat-

ing all stages of certiﬁcation processes for poultry and

swine farming in Rio Grande do Sul. It helps organize

production activities while ensuring compliance with

Brazilian animal health regulations. Veterinary certi-

ﬁcation processes depend on the model’s ability to in-

terpret complex Brazilian legislation on animal health

(Descovi et al., 2021).

The ﬁrst deployment of this integration occurred

during the IAAP outbreak in Rio Pardo, RS, Brazil,

in February 2024 (Secretaria da Agricultura, Pecu

aria

e Desenvolvimento Rural, 2024), resulting in a sig-

niﬁcant reduction in analysis time from hours to min-

utes and improving ﬁeld team management through

real-time task allocation and monitoring. Its effec-

tiveness was further validated during the containment

of a Newcastle disease outbreak in Anta Gorda, RS,

Brazil, in July 2024 (Rural, 2024), demonstrating the

system’s ability to provide rapid, cost-effective re-

sponses while maintaining rigorous biosafety proto-

cols.

The paper is structured as follows. Section 2

presents the main concepts related to the proposal.

Section 3 presents an analysis on the related work.

Section 4 presents the deﬁnition of the proposed on-

tology and the integration of semantic deﬁnitions with

an information system for response to events. Section

5 presents the evaluation process of the proposal and

Section 6 presents the conclusions of this work and

future directions.

2 BACKGROUND AND

MOTIVATION

The increasing frequency and severity of animal

health events, such as outbreaks of foot and mouth

disease, African swine fever, and avian inﬂuenza,

highlight the urgent need for advanced tools to sup-

port the management and mitigation of such crises.

These events pose signiﬁcant risks to public health,

food security, and economies worldwide, requiring

precise coordination, rapid decision-making, and ef-

fective allocation of resources. In this context,

context-aware systems and ontologies have emerged

as promising approaches to address these challenges.

2.1 The Animal Health Defense

Platform (PDSA-RS)

Established in 2019, the Plataforma de Defesa

Sanit

aria Animal do Rio Grande do Sul (PDSA-RS)

is a specialized platform launched to support and reg-

ulate animal health and production within the state’s

poultry and broader livestock sectors. Developed

with contributions from the Federal University of

Santa Maria (UFSM) and the Ministry of Agricul-

ture (MAPA), and supported by the Fundo de Desen-

volvimento e Defesa Sanit

aria Animal (Fundesa), the

PDSA-RS provides a real-time digital management

solution for tracking health certiﬁcations and facili-

tating the export and movement of avian genetic ma-

terial. This platform not only aims to improve trace-

ability but also enhances biosecurity and compliance

with sanitary regulations, an essential aspect in re-

gions with high export volumes, such as Rio Grande

do Sul.

The system’s modules, such as the poultry health

certiﬁcation feature, streamline data collection and

certiﬁcation issuance, linking veterinary inspections,

laboratories, and agricultural defense authorities.

This interconnected system helps ofﬁcials monitor

disease control in ﬂocks and facilitates efﬁcient re-

sponses to health risks. The PDSA-RS allows in-

spectors and producers to follow up on health tests,

sample processing, and the issuance of certiﬁcates re-

quired for both domestic and international movement

of poultry, ensuring that health standards are met con-

sistently.

As illustrated in Figure 1 ,the platform adopts

a microservices-oriented architecture. The frontend

comprises several specialized portals tailored for dif-

ferent stakeholders: the State Veterinary Service

(SVE), technical managers (RTs), agricultural lab-

oratories, and the Ministry of Agriculture (MAPA).

On the backend, the architecture differentiates be-

tween two distinct types of REST APIs. The business

APIs manage the core logic and processes associated

with the platform’s regulatory functions, ensuring that

workﬂows and data management align with speciﬁc

legal and procedural requirements. In contrast, the

service APIs provide more generic functionality, sup-

porting integration and interoperability with the busi-

ness APIs by delivering reusable services across the

platform.

Towards an Ontology-Based Approach for Enhancing Animal Sanitary Event Management

631

Figure 1: PDSA-RS Architecture.

The responsibility for animal sanitary control

in Brazil is shared between federal and state of-

ﬁcial veterinary services, demanding close coordi-

nation and collaboration among multiple stakehold-

ers. In Rio Grande do Sul state, the Departamento

de Vigil

ancia e Defesa Sanit

aria Animal (DDA),

which operates under the Secretaria da Agricultura,

Pecu

aria, Produc¸

ao Sustent

avel e Irrigac¸

ao do Rio

Grande do Sul (SEAPI-RS), faced signiﬁcant chal-

lenges in organizing ﬁeldwork and collecting infor-

mation during sanitary events, particularly in out-

breaks of high-pathogenic. Multiple software sys-

tems were being used to compile data, manage activi-

ties, and geographically distribute information; while

many were publicly available, they lacked integra-

tion. Additionally, the distinct sanitary status of each

zone—contaminated or not—required ﬁeld teams to

be speciﬁcally assigned, further complicating the pro-

cess.

Recognizing the need for a more efﬁcient and

streamlined system, the technical team at the Univer-

sidade Federal de Santa Maria (UFSM) and SEAPI-

RS collaborated to develop a comprehensive module

within the Plataforma de Defesa Sanit

aria Animal

(PDSA-RS). This module integrates registration data

with geo-analysis tools to manage sanitary events ef-

fectively, without requiring specialized knowledge in

information technology or geographic analysis. Mo-

tivated by this imperative, the Geo-analysis Module

was created, featuring a suite of resources divided into

four main areas: General Analysis (AG), Focus Anal-

ysis (AnF), Focus Response (RF), and Actions at Fo-

cus (AF).

2.2 Context-Aware Systems

Context-aware systems, also known as context-

sensitive systems, are designed to capture and process

environmental information and adapt their behavior

accordingly (Dey et al., 2001). Their ability to oper-

ate effectively in dynamic environments makes them

ideal for managing situations where conditions can

change rapidly, such as during animal health crises.

Context, in the scope of context-aware systems,

refers to any information that can characterize the sit-

uation of an entity, where an entity can be a person,

a place, or an object that is relevant to the interaction

between a user and the system, including the user and

the system themselves (Dey et al., 2001). For exam-

ple, in animal health management, context may in-

clude farms being inspected, current team status, ge-

olocation of disease outbreaks, or weather conditions.

Key characteristics of context-aware systems in-

clude:

• Context Capture. Collecting and processing data

from the environment, such as location, resource

status, or user behavior. In the case of animal

health events, this may involve tracking properties

to be inspected, monitoring team status, and ana-

lyzing disease spread data (Perera et al., 2014).

• Adaptation. Adjusting system behavior based on

the captured context. For instance, dynamically

recalibrating an inspection team’s route when a

new disease outbreak is detected (Abowd and My-

natt, 2000).

• Decision-Making. Integrating information to de-

termine optimal actions. For animal health man-

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

632

agement, this might involve prioritizing high-risk

properties or optimizing resource allocation (Noy

et al., 2001).

By leveraging these capabilities, context-aware

systems reduce operational costs and improve efﬁ-

ciency, enabling more agile and effective responses

during critical events.

2.2.1 Activities in Context-Aware Systems

In context-aware systems, an activity refers to a series

of actions or tasks that are dynamically adjusted based

on the captured context. These activities are adaptive,

meaning they change in response to environmental or

situational shifts. Here are a few examples:

• Real-time Animal Health Monitoring. In an

animal health management system, monitoring

activities may involve collecting data on animal

health. If a system detects a behavioral change or

signs of illness in an animal, it may automatically

trigger an activity to alert the inspection team and

direct them to speciﬁc areas (Terence et al., 2024).

This context-driven activity helps detect disease

outbreaks early and allows for a rapid response.

• Disaster Response in Risk Zones. In a disas-

ter management system, evacuation activities can

be adjusted in real-time based on weather con-

ditions and population movements. The system

adapts evacuation routes according to newly col-

lected data on the severity of a hurricane or wild-

ﬁre, ensuring that people are guided to the safest

areas (Moreno et al., 2015).

• Dynamic Inspection Tasks. During disease out-

breaks, such as avian inﬂuenza, context-aware

systems may adjust the priority of inspection tasks

based on outbreak location and risk severity. If

a new high-risk area is identiﬁed, the system can

prioritize inspections in that area, improving the

efﬁciency of disease control efforts (Abowd and

Mynatt, 2000).

2.3 Ontologies

Ontologies are formal structures that represent

domain-speciﬁc knowledge by organizing concepts,

properties, and relationships in a standardized and

comprehensible manner (Guarino et al., 2009).

Widely used in knowledge engineering, ontologies

provide a robust foundation for modeling complex

systems.

Ontologies typically consist of the following com-

ponents:

• Classes. Representing entities or concepts within

a domain, such as ”Farm,” ”Inspection Team,” or

”Activity” in the context of animal health (Bizer

et al., 2023).

• Relationships. Connecting classes and deﬁning

interactions between them, such as an inspection

team ”visiting” a property.

• Properties. Describing attributes of classes, such

as a property having a “name” or a team being

associated with a “trackable vehicle.”

• Axioms. Establishing logical rules and con-

straints to deﬁne relationships and system behav-

ior.

In context-aware systems, ontologies facilitate

semantic reasoning, enabling the extraction of ac-

tionable insights from diverse data sources. This

structured representation enhances decision-making

by standardizing and organizing critical informa-

tion, such as inspection activities, resource allocation,

and relationships between stakeholders (Chen et al.,

2003).

2.4 Animal Health Events

Animal health events have signiﬁcant impacts on

both local and global scales, particularly in countries

whose economies heavily depend on agriculture and

livestock production, such as Brazil. Outbreaks of

diseases like foot-and-mouth disease, avian inﬂuenza,

and Newcastle disease are prime examples of threats

that can compromise public health, directly affect

food security, and result in billions of dollars in eco-

nomic losses. These losses extend beyond the mor-

tality of animals, encompassing costs associated with

sanitary controls, trade restrictions, and reduced agri-

cultural productivity (Cardenas et al., 2024).

Foot-and-mouth disease, for instance, affects

cloven-hoofed animals such as cattle, sheep, and pigs.

It is highly contagious and can spread rapidly among

susceptible populations (Grubman and Baxt, 2004).

The foot-and-mouth outbreak in Germany in January

2025 exempliﬁed the swift and far-reaching conse-

quences of such events, leading to signiﬁcant trade

restrictions and economic disruptions (BBC, 2025).

Avian inﬂuenza (AI) and Newcastle disease (ND)

are critical threats to poultry production due to the

high density of bird populations and the rapid trans-

mission of infectious agents. AI is caused by in-

ﬂuenza A viruses, with highly pathogenic strains

(HPAI) resulting in high mortality rates among birds

and posing zoonotic risks in certain cases. ND,

caused by an avian paramyxovirus, is equally devas-

tating, presenting symptoms such as respiratory dis-

Towards an Ontology-Based Approach for Enhancing Animal Sanitary Event Management

633

tress, neurological manifestations, and decreased egg

production. Both diseases underscore the importance

of rapid surveillance, stringent biosecurity measures,

and immediate response efforts to minimize their im-

pacts (Swayne, 2009; Alexander, 2012).

Given the dynamic nature of animal health

crises, where new disease outbreaks or environmental

changes can emerge unexpectedly, effective coordina-

tion is necessary. The integration of ontologies within

context-aware systems provides a structured represen-

tation of the elements involved, supporting decision-

making and the execution of activities with increased

efﬁciency. Rapid response is particularly important

for poultry operations, as diseases can spread quickly

through close contact, shared equipment, and trans-

portation networks.

These outbreaks emphasize the importance of

early detection systems and effective management

strategies to minimize economic losses and prevent

further complications. The use of digital tools and

ontology-based approaches plays a key role in ad-

dressing the complexity and urgency of these events.

3 RELATED WORK

The integration of context-aware systems and ontolo-

gies has gained prominence as a means of improving

efﬁciency and decision making in various domains,

including animal health. Several studies highlight

the potential and applications of these technologies in

addressing challenges related to disease surveillance,

data interoperability, and operational management.

One notable contribution is the Animal Health

Surveillance Ontology (AHSO), which provides a

framework designed to improve data interoperability

and support surveillance activities in the domain of

animal health. This ontology formalizes the repre-

sentation of surveillance-related knowledge, enabling

better integration and analysis of diverse data sources

to inform decision-making processes (D

orea, 2019).

In the realm of syndromic surveillance, a system-

atic review of the literature on syndromic surveillance

of animal health examines various systems used in

veterinary medicine. The review evaluates innova-

tive biosurveillance systems, particularly their effec-

tiveness in monitoring equine health. These systems

demonstrate the role of advanced technologies in the

early detection and monitoring of health trends in ani-

mal populations, which is crucial for timely interven-

tions (D

orea and Vial, 2016).

Although not exclusively focused on animal

health, the study Context-Aware Systems: A Case

Study provides valuable insights into the application

of context-aware systems for facilitating daily ac-

tivities. By focusing on the modeling of context-

aware service platforms using ontologies, this work

offers foundational knowledge that can be adapted for

animal health monitoring and management systems

(Chihani et al., 2011).

Additionally, the work on An Ontology-Based

Context Model in Intelligent Environments proposes

a formal context model based on ontology to ad-

dress semantic representation, reasoning, and knowl-

edge sharing. This model, applied within the Service-

Oriented Context-Aware Middleware (SOCAM) ar-

chitecture, presents a robust approach that can be

tailored to meet the speciﬁc needs of animal health

surveillance and management (Gu et al., 2020).

These studies collectively demonstrate the versa-

tility and efﬁcacy of ontologies and context-aware

systems in improving the organization and manage-

ment of data, enhancing operational efﬁciency, and

supporting decision-making processes. By building

on these advancements, this work aims to develop a

specialized ontology for sanitary events involving an-

imals, integrated into a context-aware system, to fur-

ther contribute to the ﬁeld.

4 ONTOLOGY DEFINITION AND

INTEGRATION

In this study, we designed an ontology to model and

manage the complexity of sanitary events in the con-

text of animal health management. The ontology cap-

tures the essential entities and their relationships, pro-

viding a foundation for implementing context-aware

systems capable of enhancing decision-making and

operational efﬁciency during such events. Addition-

ally, we have integrated the ontology with a Java

Spring API to streamline system operations and im-

prove real-time data handling.

To develop the ontology, we used the Ontology

101 methodology (Noy et al., 2001) with the analysis

of Brazilian ofﬁcial Avian Inﬂuenza and Newcastle

disease Surveillance Plan (Rauber, 2023).

4.1 Key Concepts and Relationships

The main concepts of the proposed ontology are pre-

sented in Figure 2. The core classes are presented

below:

• SanitaryEvent. Represents the overarching

event, connecting all associated activities and risk

zones.

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

634

• Activity. Describes speciﬁc actions taken during

the event, such as inspections, disease monitoring,

or control measures.

– Collection. Refers to the collection of data dur-

ing the sanitary event, such as biological sam-

ples or environmental data.

SampleCollection. A subclass of Collec-

tion that represents the collection of biolog-

ical samples, such as blood, tissue, or swabs,

from animals at farms for diagnostic purposes.

DataCollection. A subclass of Collection

representing the collection of general data,

including environmental conditions, animal

health metrics, and other relevant information.

• Team. Represents groups of personnel tasked

with performing activities. Typically, a team con-

sists of multiple members, such as veterinary or

investigation teams.

– BarrierRoadTeam. A subclass of Team re-

sponsible for setting up road barriers to control

and monitor the movement of vehicles and in-

dividuals during sanitary events. These teams

ensure compliance with containment protocols.

– SurveillanceTeam. A subclass of Team that

performs surveillance activities, including in-

spections, monitoring disease spread, and col-

lecting relevant data at farms or other desig-

nated locations.

• Device. Represents technological tools used dur-

ing sanitary events. Devices assist teams by pro-

viding data collection and communication capa-

bilities.

– MobileDevice. A subclass of Device that in-

cludes smartphones, tablets, and other portable

communication devices used by teams for

recording data and maintaining communica-

tion.

• Sensor. Represents equipment capable of detect-

ing and measuring environmental or positional

data to assist in decision-making.

– GPS. A subclass of Sensor used for tracking

the location of vehicles, teams, and other assets

during sanitary events.

• RiskZone. Represents areas categorized based

on their level of risk during a sanitary event.

These zones help prioritize actions and allocate

resources effectively.

– BufferZone. A subclass of RiskZone repre-

senting areas surrounding infected or surveil-

lance zones to prevent the spread of disease.

– SurveillanceZone. A subclass of RiskZone

representing areas under observation for poten-

tial disease outbreaks.

– InfectedZone. A subclass of RiskZone repre-

senting areas conﬁrmed to have disease pres-

ence, requiring immediate attention and con-

tainment measures.

• Person. Represents individuals involved in the

sanitary event, such as team members or stake-

holders. This class provides a foundation for cap-

turing attributes like roles, skills, and responsibil-

ities.

– Veterinary. A subclass of Person representing

veterinary professionals responsible for animal

health assessments, diagnoses, and treatments

during sanitary events.

– AgriculturalTechnician. A subclass of Per-

son representing agricultural technicians assist-

ing in data collection, ﬁeld inspections, and dis-

ease monitoring during sanitary events.

• Farm. Denotes locations assigned to teams for

investigation and data collection. Farms are often

the focal point for sanitary events and activities.

– IndexSite. A subclass of Farm representing a

central site for the sanitary event. It serves as

a reference point for related activities or inter-

ventions.

– SecondarySite. A subclass of Farm represent-

ing farms or locations identiﬁed as secondary

sites in relation to an IndexSite. These sites are

monitored to assess the spread of disease or risk

factors.

– PotentialSite. A subclass of SecondarySite

representing a farm that has been identiﬁed as

a potential site for intervention, based on data

and other conditions.

• Vehicle. Represents the transportation resources

used by teams for ﬁeld operations. Vehicles are

typically equipped with tracking capabilities to

monitor team movements during investigations.

Key relationships between these entities include:

• assignedTo. Links a Team to the Farm they are

assigned to, indicating the geographical area un-

der investigation.

• collects. Relates a Team to the Collection they

are responsible for, representing the data or sam-

ples collected during the sanitary event.

• hasActivity. Connects a SanitaryEvent to the

speciﬁc Activity performed as part of the event.

• hasRiskZone. Connects a SanitaryEvent to the

speciﬁc RiskZone associated with the event.

Towards an Ontology-Based Approach for Enhancing Animal Sanitary Event Management

635

Figure 2: Main classes of the ontology for Context-Aware Response of Sanitary Event.

• hasCollection. Links a Farm to the Collection

performed at that location, such as data collection

or biological sampling.

• investigates. Describes the investigative actions

performed by a Team at a Farm, such as health

inspections or disease monitoring.

• neighborOf. Represents the spatial relationship

between two Farm instances, indicating their

proximity, useful for tracking disease spread.

• performsActivity. Relates a Team to a speciﬁc

Activity, such as performing ﬁeld visits or data

gathering during a sanitary event.

• usesVehicle. Connects a Team to the Vehicle

used during their ﬁeld activities for transportation

and logistics.

• visits. Links a Team to the Farm they visit during

an investigation or intervention as part of a sani-

tary event.

• hasSubActivities. Relates an Activity to its sub-

activities, representing a hierarchical structure of

actions within a SanitaryEvent.

• isWithin. Links a Farm to a RiskZone, indicat-

ing that the farm is geographically located within

a speciﬁc zone.

• containsFarm. Relates a RiskZone to the

Farm instances it encompasses, identifying farms

within the zone.

• hasPerson. Links a Team to a Person, represent-

ing team membership and roles within the sanitary

event.

• monitoredBy. Links a Vehicle to a Sensor, such

as a GPS, indicating that the vehicle’s movements

and activities are tracked through this sensor.

• utilizedBy. Links a Device to a Person, indicat-

ing the individual responsible for using the device

during a sanitary event.

4.2 Rules for Operational Logic

The ontology integrates SWRL (Semantic Web Rule

Language) rules to automate reasoning and ensure

consistency within the model. These rules are pro-

cessed via the Spring API, allowing for dynamic val-

idation of operational constraints and automation of

certain decision-making processes.

Example Rule. One example of a rule in the ontol-

ogy ensures that if a Farm is assigned to a Team and

the farm has a SampleCollection, the farm is con-

sidered a PotentialSite for further investigation. This

can be represented in SWRL as:

Farm(?f) ˆ Team(?t)

ˆ assignedTo(?t, ?f)

ˆ hasCollection(?f, ?c)

ˆ SampleCollection(?c)

-> PotentialSite(?f)

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636

Explanation

• Antecedent (Left-Hand Side)

– Farm(?f): Identiﬁes an individual ?f of type

Farm.

– Team(?t): Identiﬁes an individual ?t of type

Team.

– assignedTo(?t, ?f): States that the Team

?t is assigned to the Farm ?f.

– hasCollection(?f, ?c): Speciﬁes that the

Farm ?f has a Collection ?c associated with

it.

– SampleCollection(?c): Ensures that the col-

lection ?c is a SampleCollection, typically bi-

ological samples from the farm.

• Consequent (Right-Hand Side)

– PotentialSite(?f): Infers that the Farm ?f

is considered a PotentialSite for further inves-

tigation due to the collection of samples.

This rule ensures that farms with speciﬁc collec-

tions, such as sample collections, are identiﬁed as po-

tential sites for deeper investigation, helping prioritize

areas requiring more attention.

Implementation Notes.

• Practical Application

– If a Farm has sample collections, it will auto-

matically be marked as a PotentialSite for fur-

ther action, facilitating decision-making.

– If the Farm does not have the required collec-

tion, it will not be considered a PotentialSite,

thus reducing unnecessary investigation in non-

critical areas.

By deﬁning such rules, the ontology helps auto-

mate decision-making processes and enhances opera-

tional efﬁciency in managing sanitary events.

4.3 Integration with PDSA-RS

Throught a Java Spring API

The developed ontology was integrated into a Java

Spring API (Geo-Analysis API) as illustrated in Fig-

ure 1, enabling seamless interaction with the PDSA-

RS platform. The developed API facilitates:

• Data Storage and Retrieval. The ontology’s en-

tities and relationships are stored in a database

and are accessible through RESTful APIs for real-

time data retrieval and updates;

• Automated Reasoning. SWRL (Semantic Web

Rule Language) rules are executed via the API

to ensure operational logic is maintained, such as

ensuring teams are assigned to appropriate prop-

erties and activities are carried out according to

speciﬁed constraints;

• Real-Time Updates. Integration with dynamic

data sources, such as vehicle tracking and team

assignments, allows the system to adapt in real-

time based on operational conditions.

4.4 Applications and Beneﬁts

This ontology and its integration with the Java Spring

API facilitate the development of context-aware sys-

tems by enabling:

• Context Capture. Accurate representation of

spatial and operational data, including property

locations and team assignments, retrieved in real-

time via the API.

• Decision Support. Automated reasoning via the

API to optimize team assignments, route plan-

ning, and decision-making processes.

• Adaptability. Integration with dynamic data

sources, such as real-time vehicle tracking, which

are processed and delivered through the Spring

API to adjust to evolving conditions during san-

itary events.

By leveraging this ontology and integrating it with

a Java Spring API, organizations can improve the ef-

ﬁciency and effectiveness of sanitary event manage-

ment, ensuring more responsive and adaptable op-

erations during animal disease outbreaks. This in-

tegration also facilitates better communication and

coordination between ﬁeld teams and central com-

mand, leading to reduced economic impacts during

such events.

5 EVALUATION AND

DISCUSSION

The proposed ontology and the API that supports the

use of this ontology was integrated in the PDSA-

RS platform. We prototyped a module called Geo-

analysis. To validate the proposed ontology, the Geo-

analysis was initially validated during the manage-

ment of an avian inﬂuenza outbreak in February 2024

in Rio Grande do Sul state (RS, Brazil) (Secretaria

da Agricultura, Pecu

aria e Desenvolvimento Rural,

2024).

The results demonstrated the effectiveness of the

module, drastically reducing the time spent on data

analysis from 3 to 4 hours per day with other tools to

Towards an Ontology-Based Approach for Enhancing Animal Sanitary Event Management

637

just a few minutes when using the platform. This al-

lowed for faster and more efﬁcient decision-making,

optimizing the management of resources and reduc-

ing the workload on ﬁeld teams.

5.1 The Geo-Analysis Module

The system provides various capabilities to manage

the response to sanitary events. Below are the main

areas of functionality:

5.1.1 General Analysis (AG) and Focus Analysis

(AnF)

The General Analysis (AG) and Focus Analysis

(AnF) areas provide a comprehensive view of the san-

itary event management. The General Analysis (AG)

area allows the visualization of registered rural prop-

erties on a map, along with the ability to insert the

Rural Environmental Registry (CAR) shapes for area

evaluation. An example of the visualization of an in-

fected farm an the animal movement in Geo-Analysis

module is presented in Figure 3.

The Focus Analysis (AnF) area allows for the

identiﬁcation of sanitary focus points, which could be

a rural property or a speciﬁc geographical location,

with the ability to create up to three zones (perifo-

cus, surveillance, and protection). Additionally, ani-

mal movement history is available for analysis.

5.1.2 Focus Response (RF)

The Focus Response (RF) area manages the contain-

ment efforts of the sanitary event, linking Ofﬁcial Vet-

erinary Service teams to the focus, assigning tasks,

and monitoring the progress of information collection

through checklists. An example of the planning of

teams to visit the farms is presented in Figure 4.

5.1.3 Actions at Focus (AF)

The Actions at Focus (AF) area offers a real-time

dashboard to track activities, pending tasks, existing

focus points, and involved teams, allowing for better

management of ﬁeldwork and providing feedback for

continuous improvement. An example of monitoring

visits in AF is presented in Figure 5.

The platform’s success lies in its simplicity and

adaptability, allowing ﬁeld teams to work effectively

without needing advanced technological expertise.

Additionally, real-time updates ensure that the man-

agement of the sanitary event adapts to changing con-

ditions, improving overall effectiveness.

By utilizing the platform, the response to the san-

itary event was rapid and efﬁcient, with a rational use

of human and ﬁnancial resources while maintaining

satisfactory levels of biosafety, as prescribed by best

practices in emergency management.

5.2 First Evaluation - IAAP - Rio Pardo

- RS

The tool was ﬁrst used in the response to the IAAP

outbreak in Rio Pardo, RS, Brazil, in February 2024,

demonstrating its effectiveness in managing the san-

itary event. It signiﬁcantly reduced the analysis time

from 3 to 4 hours daily with other tools to just min-

utes using the Platform. In addition to information

analysis, the tool enabled better management of the

teams involved across various control areas, allow-

ing for more accurate estimation of team sizes and

better task distribution. As a result, the response to

the outbreak in Rio Pardo was both quick and effec-

tive, with a rational use of human and ﬁnancial re-

sources, ensuring activities remained at a satisfactory

level of biosafety, in accordance with best practices

for emergency management (Secretaria da Agricul-

tura, Pecu

aria e Desenvolvimento Rural, 2024).

5.3 Second Evaluation - NewCastle

Disease - Anta Gorda - RS

The tool was used during the Newcastle disease

outbreak in Anta Gorda, RS, Brazil, in July 2024,

demonstrating its functionality in managing sanitary

events. The platform supported containment efforts

and facilitated the coordination of involved teams. Its

capability to process information and manage tasks

contributed to an organized and efﬁcient response to

the outbreak. This included structuring ﬁeld team

operations and supporting decision-making regarding

resource allocation, ensuring timely actions aligned

with sanitary event management protocols (Ala’a

et al., 2024; Rural, 2024).

The System Usability Scale (SUS) was used to

evaluate the usability of the system. A total of 29

participants completed the SUS survey, answering a

series of 10 questions designed to assess the ease of

use, complexity, and conﬁdence in using the system.

5.4 Survey Results

The SUS survey consisted of 10 questions, each rated

on a scale from 1 to 5. For the calculation of the

SUS score, the responses to the odd-numbered ques-

tions (1, 3, 5, 7, 9) were adjusted by subtracting 1

from the given score, while the responses to the even-

numbered questions (2, 4, 6, 8, 10) were adjusted by

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

638

Figure 3: General Analysis (AG) and Focus Analysis (AnF) - Visualization of rural properties, CAR shapes, sanitary focus

points, and animal movement history.

Figure 4: Focus Response (RF) - Management of contain-

ment efforts and team assignments.

subtracting the score from 5. The adjusted scores

were then summed for each participant, and the av-

erage score was calculated.

The SUS scores were calculated using the ad-

justed ratings for each participant. The table (Table

1) shows the responses for all 29 participants, includ-

ing their ratings for each of the 10 questions.

Figure 5: Actions at Focus (AF) - Real-time dashboard for

activity tracking and team management.

5.5 SUS Score Calculation

To calculate the overall SUS score, the adjusted re-

sponses for each participant were summed and aver-

aged. The formula for calculating the SUS score is as

follows:

Towards an Ontology-Based Approach for Enhancing Animal Sanitary Event Management

639

Table 1: SUS Questionnaire Results.

Participant Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10

P1 5 1 5 1 5 1 5 1 5 1

P2 5 1 5 4 5 2 5 2 4 2

P3 5 2 5 4 4 2 5 1 4 1

P4 5 1 5 2 4 2 4 1 4 1

P5 4 2 4 2 4 2 4 1 4 1

P6 4 1 5 1 4 1 5 1 4 1

P7 5 2 4 3 3 2 5 1 4 1

P8 4 1 5 3 4 1 5 1 4 1

P9 5 3 4 4 4 3 5 3 4 4

P10 5 2 4 4 4 3 3 2 3 2

P11 5 1 1 1 5 1 5 1 5 1

P12 4 1 5 1 4 1 5 2 4 1

P13 4 1 3 2 2 3 3 1 3 4

P14 5 1 4 3 5 1 5 1 5 1

P15 5 1 5 3 5 1 4 1 5 2

P16 5 1 5 1 4 1 5 1 5 2

P17 5 2 4 2 4 2 4 1 5 1

P18 4 2 4 2 3 2 4 2 4 2

P19 5 1 5 1 5 1 5 1 5 1

P20 4 1 5 1 4 1 5 1 5 1

P21 4 1 5 1 4 2 5 1 4 1

P22 5 1 5 1 4 1 5 1 4 1

P23 5 1 5 1 5 1 5 1 5 1

P24 5 2 4 2 5 1 5 1 4 4

P25 5 1 5 1 5 1 5 1 5 2

P26 5 5 4 3 3 2 5 2 3 1

P27 5 1 5 1 4 1 4 1 4 1

P28 3 3 3 4 3 3 3 3 3 3

P29 5 5 5 4 5 4 5 1 5 4

SUS Score =



∑

Adjusted Scores

Number of Respondents



× 2.5

Where the adjusted score for each item is calcu-

lated as:

Adjusted Score =

(

Score − 1 for odd-numbered items

5 − Score for even-numbered items

For example, for a participant who answers: -

Question 1 with a score of 5, the adjusted score would

be 5 − 1 = 4. - Question 2 with a score of 1, the ad-

justed score would be 5 − 1 = 4.

After calculating the adjusted scores for all the

questions, the total score for each participant is cal-

culated, and then the average score across all partici-

pants is determined.

The higher the SUS score, the more usable the sys-

tem is considered to be. A typical range for a SUS

score is between 0 and 100, with scores above 68 gen-

erally considered above average and those below 68

considered below average.

Based on the SUS score, we can infer that the sys-

tem received a positive evaluation from the partic-

ipants, with a calculated SUS score of 75.52, indi-

cating that the system is considered easy to use and

well-integrated overall. Most users expressed con-

ﬁdence in using the system, though there were some

areas where improvement could be made, especially

in reducing complexity and increasing ease of use for

new users.

6 CONCLUSION

This study evaluated the usability of the system

through the System Usability Scale (SUS), with the

results indicating a positive response from the partic-

ipants. The calculated SUS score of 75.52 suggests

that the system is considered easy to use and well-

integrated. Most users reported conﬁdence in using

the system, though there are areas that could be im-

proved, particularly in simplifying the user interface

and increasing the ease of use for new users. The eval-

uation provides insights into the system’s current per-

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

640

formance and areas for further reﬁnement.

Moving forward, we plan to further enhance the

system by reﬁning its contextual modeling to bet-

ter reﬂect real-world sanitary management processes.

This includes improving the ontology to more accu-

rately represent the dynamics of disease control and

agricultural management. Additionally, we will de-

velop and implement new adaptive rules within the

system, allowing it to respond more effectively to di-

verse outbreak scenarios. These improvements aim to

streamline the user experience while ensuring the sys-

tem remains practical, responsive, and aligned with

the needs of its users in managing sanitary events.

ACKNOWLEDGMENTS

This research is supported by FUNDESA, project

“Combining Process Mapping and Improvement with

BPM and the Application of Data Analytics in

the Context of Animal Health Defense and Inspec-

tion of Animal-Origin Products in the State of RS”

(UFSM/060496) and FAPERGS, grant n. 24/2551-

0001401-2. The research by Vin

ıcius Maran is par-

tially supported by CNPq grant 306356/2020-1 DT-

2, CNPq PIBIC and PIBIT program and FAPERGS

PROBIC program.

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