Mining ontologies to extract implicit knowledge

• Ano de defesa: 2016
• Autor(a) principal: Navarro, Lucas Fonseca
• Orientador(a): Appel, Ana Paula
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• Palavras-chave em Português: Mineração de dados; Ontologia; Bases de conhecimento; Algoritmos
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/8152

With the exponentially growing of data available on the Web, several projects were created to automatically represent this information as knowledge bases(KBs). Knowledge bases used in most projects are represented in an ontology-based fashion, so the data can be better organized and easily accessible. It is common to map these KBs into a graph to apply graph mining algorithms to extract implicit knowledge from the KB, knowledge that sometimes is easy for human beings to infer but not so trivial to a machine. One common graph-based task is link prediction, which can be used not only to predict edges (new facts for the KB) that will appear in a near future, but also to nd misplaced edges (wrong facts present in the KB). In this project, we create algorithms that uses graph-mining (mostly link-prediction based) approaches to nd implicit knowledge from ontological knowledge bases. Despite of common graph-mining algorithms, we mine not just the facts on the KB, but also the ontology information (such as categories of instances and relations among them). The implicit knowledge that our algorithms will nd, is not just new facts for the KB, but also new relations and categories, extending the ontology as well.