Brazil

Ontologies are used in Information Systems to manage and share knowledge pertaining to different domains, considering it is common that one particular piece of knowledge is modeled individually into different ontologies. Alignment processes are carried out towards interoperability; however, the results are not effective in most cases, requiring manual repair and review by domain experts. Thus, this work introduces a repair and review tool for the alignment of ontologies with the goal
of assisting experts in the alignment repair tasks. Using the technology acceptance model, a proof of concept was made to analyze the perception by domain experts regarding the tool’s usefulness, ease, and intention of use. From statistical analyses in terms of reliability, default path, inference, and correlation, it has been found that study participants had a positive perception of the tool’s usefulness and ease in the tasks of ontology alignment repair. Therefore, the tool and its functionalities
proved to be useful towards assisting domain experts in their tasks of reducing the semantic heterogeneity among ontologies pertaining to the same domain through alignment repair.

BRACIS 2020

A Useful Tool to Support the Ontology Alignment Repair

proof of concept.

technology acceptance model

ontology alignment repair

ontology matching

data visualization

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)*<br><br>

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EACL 2026

Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

technical paper

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

Most ensemble methods for classification and regression tasks are based on a simple naive technique such as bagging, where all models' outputs are combined in a single last label. But in this paper, we evaluate a meta-learning approach that for each instance a different subset of classifiers is chosen.

Towards an Instance Level Meta-Learning Based Ensemble for Time Series Classification

A short-term electricity demand forecasting in the Southern Region of Brazil using the ARIMA Model and the Holt Exponential Smoothing Model (SEH)

I will present the Doclass, a free and open-source software for the Web that aims to assist in labeling and classifying large sets of documents. As a result, the first version of a system with an architecture composed of a mobile frontend that communicates with a backend through a REST API was published, with satisfactory performance evaluation by the applicant. Other results involve the use of active learning techniques to reduce human effort when performing the classification of documents, as well as the Uncertainty strategy to choose the document to be labeled.

Doclass: open-source software to support document labelingand classification

My presentation is about my solution to this year's KDD competition. There I cover the models, the methods of feature selection, validation, parameters optimization and also my final ensemble.

Solution KDD BR Competition 2020

O vídeo descreve o desafio da competição KDD-BR 2020, que trata da predição de períodos de indisponibilidade de aluguel de carros. Ele foi elaborado pela equipe de Ciência de Dados da empresa Localiza, que foi a proponente do problema.

KDD-BR: apresentação do problema pela empresa Localiza

KDD-BR 2020 solution using 1-dimensional convolutions.

Car availability prediction in rental angencies [KDD-BR 2020]

As commodities milho e soja são produtos consumidos em grande escala no mundo. As flutuações nos preços de mercado têm efeitos de longo alcance sobre os consumidores, agricultores e processadores de grãos. Assim, a previsão dos preços desses grãos tem atraído significativa atenção dos pesquisadores. Os modelos de previsão geralmente usam dados quantitativos de série temporal. No entanto, fatores qualitativos externos podem influenciar os dados em séries temporais, como eventos políticos, crises econômicas e o mercado de câmbio estrangeiro. Essas informações não são explícitas nos dados da série temporal e esses fatores podem influenciar a previsão dos valores das variáveis. Os dados textuais extraídos de notícias, fóruns e redes sociais podem ser uma fonte de conhecimento sobre fatores externos e potencialmente úteis para modelos de previsão de séries temporais. Alguns estudos apresentam técnicas de mineração de texto para combinar dados textuais com séries temporais. Porém, as representações existentes apresentam algumas limitações, como a maldição da dimensionalidade e atributos ineficazes. Este trabalho aplica métodos de pré-processamento em séries temporais e usa representações combinadas com dados textuais para prever o preço futuro do milho e da soja. Os resultados indicam que os métodos utilizados podem ser uma alternativa para melhorar o desempenho de previsão em tarefas de regressão.

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Towards an Instance Level Meta-Learning Based Ensemble for Time Series Classification

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Large Language Models as Oracles for Ontology Alignment

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Next from BRACIS 2020

Towards an Instance Level Meta-Learning Based Ensemble for Time Series Classification

Similar lecture

Large Language Models as Oracles for Ontology Alignment

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads