Ontology is an interdisciplinary field that studies concepts and theories that provide the foundation for the construction of shared conceptualizations of specific domains. In recent years, we have seen a growing interest in the application of ontologies to solve modeling and classification problems in various areas such as: Computer Science, Information Science, Philosophy, Artificial Intelligence, Linguistics, Knowledge Management, Biomedicine, Semantic Web, among others.

The Seminar on Ontology Research in Brazil (ONTOBRAS) envisions an opportunity to provide a scientific environment where researchers and professionals from the aforementioned fields can exchange knowledge about theories, methodologies, languages, tools, and experiences related to the theoretical foundation, development, and application of ontologies.

In its 18th edition, ONTOBRAS 2025 is part of a tradition that has existed in Brazil since 2005. The Brazilian Ontology Community defined it as the unique, scientifically qualified forum for presenting and discussing ontologies and application cases in Brazil.

This edition of Ontobras will focus on “The Influence of Ontologies in Generative AI.” This topic is of great general interest both in academia and the productive sector. Recent studies have shown how ontologies and knowledge graphs have contributed to improving the quality of responses from generative AI. This theme will also bring academia and the productive sector together to advance research, partnerships, and the development of applications and products.

Submissions through easychair: https://easychair.org/conferences/?conf=ontobras2025

Important dates

- Submission Deadline: ~~June 22nd, 2025~~ July 13th, 2025 (hard deadline)
- Author notification: ~~July 31st, 2025~~ August 12th, 2025
- Camera ready: ~~August 22nd, 2025~~ September 9th, 2025
- Authors’ Registration Deadline: August 29th, 2025

Topics of interest

Topics of interest for the event include, but are not limited to:

- Ontologies and Conceptual Modeling:
  - Ontological foundations for conceptual modeling and metamodeling
  - Foundational and upper ontologies
  - Semantic consistency
  - Ontology-based conceptual modeling tools and environments

- Ontologies and knowledge organization:
  - Facet Theory and Theory of Concepts
  - Terminology, Folksonomies, Thesauri, Taxonomies, Metadata
  - Documentary languages
  - Knowledge discovery and reasoning
  - Knowledge representation and management
  - Ontology governance

- Ontology Engineering:
  - Methodologies, techniques,practices, languages and tools
  - Composition and modularity
  - Interoperability between ontologies, integration, mapping and alignment
  - Ontological language interoperability
  - Ontology patterns and anti-patterns
  - Ontology evaluation and validation
  - Integration problems, practices and methods
  - Ontology Enrichment and semantic enrichment
  - (Semi-)automated ontology development

- Semantic Web:
  - Modeling
  - Information retrieval
  - Ontology-based search
  - Linked open data (LOD) applications
  - Ontology and Natural Language Processing (NLP)
  - Linguistic ontologies applied to text processing
  - Computational linguistics
  - Access, integration and exchange of data based on databases and ontologies on the Web
  - Knowledge graphs (construction, maintenance, reasoning) and virtual knowledge graphs
  - Social and human aspects of the Semantic Web

- Ontology Applications:
  - Ontology for e-science, life sciences, e-business, multimedia and cultural heritage
  - Domain ontologies (Education, Health, Smart Cities, Government and others)
  - Ontology-driven information system design
  - Ontology-driven business modeling
  - Ontology tools: construction, reasoning, evaluation
  - Data management and FAIR principles

- Ontologies and data science, machine learning, artificial intelligence
  - Ontology-based data selection and preprocessing
  - Semantic annotation and dataset enrichment
  - Ontology-based constraints and semantics in machine learning models
  - Neuro-symbolic hybrid models based on ontologies
  - Ontology for enhancing generative AI
  - Ontology for knowledge-based explanation of machine learning models
  - Ontologies as support for auditing model decisions
  - Ontologies as support for bias, completeness, and consistency verification in AI systems

- Ontology Visualization
  - Visualization as support for ontology engineering tasks

- Ontology Analysis
  - Structural and formal analysis of ontologies
  - Semantic and conceptual analysis of ontologies and their domain adequacy
  - Quality and maturity assessment of ontologies
  - Empirical analysis based on ontology usage
  - Tools and environments for ontology analysis

Submission Guidelines

Submissions should be written in ENGLISH (preferred), PORTUGUESE or SPANISH. We strongly encourage authors to submit their papers in ENGLISH to reach readers in many different countries. Submissions in PORTUGUESE or SPANISH must have an abstract and keywords in ENGLISH.

All submissions will undergo peer review by experts in the field.

All papers must be original and cannot have been submitted simultaneously to another journal or conference. The following types of papers are welcome (page limits exclude references):

- Full papers (up to 12 pages): High-quality papers on various topics focused on consolidated ontology research contributions. They may be even more specialized, theoretical, technical, and practical research contributions that directly or indirectly address or are related to the areas listed below. They may also be papers by advanced students whose dissertations or theses are in the final stages and have not been completed by the submission date but have already shown substantial results.
- Short articles (up to 6 pages): These are usually student papers, but they may also discuss new ideas that are in an early stage of development or report partial results of ongoing research, dissertations, or theses.

Authors should indicate the type of paper they will submit. Additionally, it is suggested to indicate as a footnote on the first page of the type of the paper.

All submissions must be in Adobe Portable Document Format (PDF) and must follow the CEUR-ART template, single column. Templates for Microsoft Office Word, Open Office and LaTeX can be downloaded at: https://www.inf.ufrgs.br/ontobras/wp-content/uploads/2025/05/CEUR-Template-1col.zip.

Best Paper Award: The ONTOBRAS Program Committee will select a full paper to receive the Best Paper Award.

Review Process

ONTOBRAS adopts a double-blind review process. Thus, submitted papers must conceal the names and affiliations of the authors. In addition:

- Citations to the authors’ own work should be written in the third person (e.g., use “in the work of Fulano et al.” instead of “in our work”);
- If the submitted paper is an evolution of the authors’ previous work, one can anonymize the reference in the References section. For example, instead of “this paper evolves the proposal of Fulano et al.”, one can use “this paper evolves the proposal presented in [ref]” and the reference [ref] can be presented as “[ref] Reference omitted due to double-blind review” in the References section;
- The use of terms that allow the identification of authors (e.g., university or project names) should be avoided;
- If the paper includes links to artifacts related to the work, repositories or websites that allow the identification of authors should not be used. Research artifacts should be provided anonymously.

If the article is accepted, information omitted in the submitted version must be included in the final version.

Desk Rejection: Submissions that are outside the scope of ONTOBRAS or do not comply with the required submission format and double-blind rules will be rejected without review.

Publication

Accepted papers will be published in the proceedings of ONTOBRAS 2025 at CEUR-WS as part of the IAOA series.

- Publication of accepted papers requires registration of at least one of the authors at ONTOBRAS and presentation of the paper during the event.
- The presentation of accepted papers will be in person.

Program Committee – Main Track

- Alcides Lopes
- Alexandre Rademaker (EMAp/FGV)
- Amanda Damaceno de Souza (FUMEC University)
- Ana Carolina S. Arakaki (UnB)
- Camila Z. Aguiar (UFES)
- Carlos Marcondes (UFF, Graduate Program in Information Science)
- Cassia Trojahn (UT2J & IRIT)
- Claudenir M. Fonseca (UT)
- Cláudio Gottschalg-Duque (UnB)
- Clever Farias (USP)
- Cristine Griffo (EURAC)
- Daniela Lucas Da Silva (UFES)
- Eduardo Felipe (UNIFEI)
- Evellin Cardoso (UFG)
- Fabricio Henrique Rodrigues (UFRGS)
- Felipe Augusto Arakaki (UnB)
- Fernanda Baião (PUC-Rio)
- Fernanda Farinelli (UnB)
- Fernando Cruz (UnB)
- Fernando Ostuni Gauthier (UFSC)
- Ferrucio de Franco Rosa (CTI / UNIFACCAMP)
- Flavio S. Correa Da Silva (USP)
- Fred Freitas (UFPE)
- Gabriela Henning (INTEC)
- Giancarlo Guizzardi (UT)
- Ítalo Oliveira (UT)
- J. Neil Otte (Johns Hopkins University)
- Jeanne Louize Emygdio (UFMG)
- Joao Lima (Federal Senate)
- João Paulo Almeida (UFES)
- Joel Carbonera (UFRGS)
- John Beverley (University at Buffalo)
- José Parente (ITA)
- Julio Cesar Dos Reis (UNICAMP)
- Julio Cesar Nardi (IFES)
- Lais Salvador (UFBA)
- Luan Garcia (UFRGS)
- Luciano Heitor Gallegos Marin (UFPR)
- Luís Ferreira Pires (UT)
- Mara Abel (UFRGS)
- Marcello Bax (UFMG)
- Maria das Graças da Silva Teixeira (UFES)
- Mathias Brochhausen (UAMS)
- Monalessa Barcellos (UFES)
- Pedro Paulo F. Barcelos (Health-RI)
- Rafael Peñaloza (UNIMIB)
- Regina Braga (UFJF)
- Renata Guizzardi (UT)
- Sandro Rama Fiorini (IBM)
- Silvio Gonnet (INGAR – CIDISI)
- Thiago Henrique Bragato Barros (UFRGS)
- Tiago Prince Sales (UT)
- Veruska Zamborlini (UFES)
- Vitor E. Silva Souza (UFES)
- Vivian S. Silva (UFRJ)