Classificação de informação usando ontologias

• Ano de defesa: 2006
• Autor(a) principal: Silva, Eunice Palmeira da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Alagoas; BR; Modelagem Computacional de Conhecimento; Programa de Pós-Graduação em Modelagem Computacional de Conhecimento; UFAL
• Palavras-chave em Português: Artificial intelligence; Information classification; Ontologies; Inteligência artificial; Sistemas de recuperação da informação - Classificação; Ontologia; Sistemas multiagentes; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO::COMPUTABILIDADE E MODELOS DE COMPUTACAO
• Link de acesso: http://repositorio.ufal.br/handle/riufal/852

Although the positive aspects that Internet possesses and the potential it permits, there is a problematic that consists on finding needed pieces of information among the deluge of available documents on the web. Tools that are able to semantically treat the information contained in the documents which follows a structure only focused on data presentation are still lacking. The MASTER-Web system solves the problem of integrated extraction of content-pages that belong to classes which form a cluster. In this context, we propose the extension of this tool to the scientific articles classification based on ontologies. To achieve this goal, an ontology for the Artificial Intelligence domain was constructed and rule-based classification strategies were adopeted. The approach presented here employs this ontology and textual classification techniques to extract useful pieces of information from the articles in order to infer to which themes it is about. This combination led to significative results: e.g. in the texts, the system is able to identify the specific subdivisions of AI and entails conclusions, distinguishing correctlly the themes of the articles from the ones that are briefiy mentioned in the texts. The application of simple techniques and a detailed ontology lead to promising classification results, independently of the document structure, proposing an eficient and plausible solution.