SciTePress - Affiliation Profile

Research.Publish.Connect.

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Affiliation

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

Context (please use complete words only)

Field

Sort by

2 Papers

Improving Large Language Models Responses with Retrieval Augmented Generation in Animal Production Certification Platforms

Topics: Industrial Applications of Artificial Intelligence; Strategic Decision Support Systems; Applications of Expert Systems

Authors: Pedro Montero , Jonas Gassen , Glênio Descovi , Vinícius Maran , Tais Barnasque , Matheus Flores and Alencar Machado

Affiliation: Laboratory of Ubiquitous, Mobile and Applied Computing (LUMAC), Federal University of Santa Maria, Brazil

Digital Object Identifier: 10.5220/0013286100003929

Proceedings of ICEIS 2025, ISBN: 978-989-758-749-8

Quick Abstract

This study explores the potential of integrating Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to enhance the accuracy and relevance of responses in domain-specific tasks, particularly within the context of animal health regulation. Our proposal solution incorporates a RAG system on the PDSA-RS platform, leveraging an external knowledge base to integrate localized legal information from Brazilian legislation into the model’s response generation process. By combining LLMs with an information retrieval module, we aim to provide accurate, up-to-date responses grounded in relevant legal texts for professionals in the veterinary health sector.

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

Topics: Industrial Applications of Artificial Intelligence; Strategic Decision Support Systems; Applications of Expert Systems; Context-aware Pervasive Systems

Authors: Felipe Machado ¹ , Jonas Gassen ¹ , Matheus Flores ¹ , Francisco Lopes ² , Fernando Groff ² , Alencar Machado ¹ and Vinicius Maran ¹

Affiliations: ¹ Laboratory of Ubiquitous, Mobile and Applied Computing, Federal University of Santa Maria, Santa Maria, Brazil , ² Departamento de Defesa Agropecuária, Secretaria da Agricultura, Pecuária e Desenvolvimento Rural, Porto Alegre, Brazil

Digital Object Identifier: 10.5220/0013464300003929

Proceedings of ICEIS 2025, ISBN: 978-989-758-749-8

Quick 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 Protégé 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.