Title: How to help end users to get better decisions? Personalising OLAP aggregation queries through semantic recommendation of text documents
Authors: Talita dos Reis Lopes Berbel; Sahudy Montenegro Gonzàlez
Addresses: Department of Computer Science, Federal University of São Carlos, Sorocaba Campus, SP, Brazil ' Department of Computer Science, Federal University of São Carlos, Sorocaba Campus, SP, Brazil
Abstract: This paper describes an effective solution for recommending textual OLAP over data warehousing environments. The recommendation process is based on text semantics and query personalisation to improve the relevance of the retrieved results. In order to aggregate and recommend documents, we need a measure of semantic similarity. The first issue we addressed was the meaning of similarity between two concepts. For this, we used ontologies and the distance between terms over the ontology based on the least common ancestor. The second issue we dealt with was the meaning of similarity between two documents. For that, we calculated the statistical metric frequency. The purpose of query personalisation is to offer to the user an interactive way for obtaining relevant aggregation of documents based on adjustable parameters. We implemented the solution for multidimensional analysis over PubMed database. In the case study, we used the Medical Subject Headings provided by the US National Library of Medicine. At the end, we present the results of some experiments that show that good recommendations are possible. The results are discussed based on the evaluation metrics: precision, recall and F1-measure.
Keywords: data warehousing; textual data; OLAP aggregation queries; text semantics; ontology; lowest common ancestor; LCA; query personalisation; Medical Subject Headings; MeSH; semantic recommendation; text documents; semantic similarity.
International Journal of Business Intelligence and Data Mining, 2015 Vol.10 No.1, pp.1 - 18
Available online: 21 Apr 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article