Recomendação semântica de documentos de texto mediante a personalização de agregações OLAP.

• Autor(a) principal: Berbel, Talita dos Reis Lopes
• Data de Publicação: 2015
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://repositorio.ufscar.br/handle/ufscar/632
• topic: data warehouse; OLAP; dados textuais; agregação; recomendação; semântica; ontologia; LCA; personalização de consultas; MeSH; Tecnologia OLAP; ontologia; semântica; data warehouse; OLAP; textual data; aggregation; recommendation; semantic; ontology; LCA; Query Personalization; MeSH; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

With the rapid growth of unstructured data, such as text documents, it becomes more and more interesting and necessary to extract such information to support decision making in business intelligence systems. Recommendations can be used in the OLAP process, because they allow users to have a particular experience in exploiting data. The process of recommendation, together with the possibility of query personalisation, allows recommendations to be increasingly relevant. The main contribution of this work is to propose an effective solution for semantic recommendation of documents through personalisation of OLAP aggregation queries in a data warehousing environment. In order to aggregate and recommend documents, we propose the use of semantic similarity. Domain ontology and the statistical measure of frequency are used in order to verify the similarity between documents. The threshold of similarity between documents in the recommendation process is adjustable and this is the personalisation that provides to the user an interactive way to improve the relevance of the results. The proposed case study is based on articles from PubMed and its domain ontology in order to create a prototype using real data. The results of the experiments are presented and discussed, showing that good recommendations and aggregations are possible with the suggested approach. The results are discussed on the basis of evaluation measures: precision, recall and F1-measure.