Uma solução efetiva para aprendizagem de relacionamentos não taxonômicos de ontologias

• Ano de defesa: 2014
• Autor(a) principal: SERRA, Ivo José da Cunha Serra
• Orientador(a): GIRARDI, Rosario
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Aprendizagem de relacionamentos não-taxonômicos; Ontologias; Processamento da linguagem natural; Aprendizagem de máquina
• Palavras-chave em Inglês: Learning non-taxonomic relationship of ontologies; Ontology; Natural language processing; Machine Learning
• Área do conhecimento CNPq: Ciência da Computação
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/1826

Learngin Non-Taxonomic Relationship is a sub-field of ontology learning and is an approach to automate the extraction of these relationships from textual information sources. Techniques for learning non-taxonomic relationships just like others in the area of Ontology Learning are subject to a great amount of noise since the source of information from which the relationships are extract is unstructured. Therefore, customizable solutions are needed for theses techniques to be applicable to the wideste variety of situations. This Thesis presents TARNT, a Techinique for Learning for Non-Taxonomic Relationship of ontologies from texts in English that employs techniques from Natural Language Processing and statistics to structure text and to select relationship that should be recommended. The control over the execution of its extraction rules and consequently on the recall and precision in the phase "Extraction of candidate relationships", the "apostrophe rule", which gives particular treatment to extractions that have greater probability to be valid ones and "Bag of labels", a refinement technique that has the potential to achieve greater effectiveness than those that operate on relationships consisting of a pair of concepts and a label, are among its positive aspects. Experimental evaluations of TARNT were performed according to two procedures based on the principle of comparing the learned relationship consisting of a pair of concepts and a label, are among its positive aspects. Experimental evaluations of TARNT were performed according to two procedures based on the principle of comparing the learned relationships with reference ones. These experiments consisted in measuring with recall and precision, the effectiveness of the technique in learning non-taxonomic relationships from two corpora in the domains of biology and family law. The results were compared to thet of another approach that uses and algorithm for the extraction of association rules in the Refinement phase. This Thesis also demonstrate the hypothesis that solutions to the Refinement phase that use relationships composed of two ontology concepts and a label are less effective than those that refine relationships composed of only two concepts, since they tend to have lower values for the evaluation measures when considering the same corpus and reference ontology. The demonstration was conducted by a theoretical exposition that consisted of the generalization of the observations made on the results obtained by two techniques that refine relationships of the two types considered.