Keyword search is typically associated with information retrieval systems. However, recently, keyword search has been expanded to relational databases and RDF datasets, as an attractive alternative to traditional database access. With this motivation, this paper first introduces a platform for data and knowledge retrieval, called DANKE, concentrating on the keyword search component. It then describes an application that uses DANKE to implement keyword search over two COVID-19 data scenarios.

Natural Language Interface to Databases (NLIDB) systems usually do not deal with aggregations, which can be of two types: aggregation functions (such as count, sum, average, minimum, and maximum) and grouping functions (GROUP BY). This paper addresses the creation of a generic module, to be used in NLIDB systems, that allows such systems to perform queries with aggregations, on the condition that the query results the NLIDB returns are or can be transformed into tables. The paper covers aggregations with specificities, such as ambiguities, timescale differences, aggregations in multiple attributes, the use of superlative adjectives, basic unit measure recognition, and aggregations in attributes with compound names.

In the first place, I would like to thank, above all, my advisor Prof. Marco Antonio Casanova for his boundless patience, for helping me find a topic that was both challenging and suitable for a master's degree and also for having guided me in the steps towards the publication of an article.Further, I would like to thank PUC-Rio and CAPES for providing a tuition scholarship. It was an honor to be part of the "Programa de Pós-graduação emInformática". This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Finance Code 001.To Michael Kelly, my friend and English guru, for helping me with the language.For my colleagues at Grupo Santa Isabel, especially Creston Fernandes, Isabel Ferraz Magalhães and Ferdinando Valle Magalhães for understanding the importance of this project for me and allowing me to dedicate the needed the time to complete it.

A Natural Language Interface to Database (NLIDB) refers to a database interface that translates a question asked in natural language into a structured query. Aggregation questions express aggregation functions, such as count, sum, average, minimum and maximum, and optionally a group by clause and a having clause. NLIDBs deliver good results for standard questions but usually do not deal with aggregation questions. The main contribution of this article is a generic module, called GLAMORISE (GeneraL Aggregation MOdule using a RelatIonal databaSE), that extends NLIDBs to cope with aggregation questions. GLAMORISE covers aggregations with ambiguities, timescale differences, aggregations in multiple attributes, the use of superlative adjectives, basic recognition of measurement units, and aggregations in attributes with compound names.

Keyword search is typically associated with information retrieval systems. However, recently, keyword search has been expanded to relational databases and RDF datasets, as an attractive alternative to traditional database access. This paper introduces DANKE, a platform for keyword search over databases, and discusses how third-party applications can be equipped with DANKE to take advantage of a data retrieval mechanism that does not require users to have specific technical skills for searching, retrieving and exploring data. The paper ends with the description of an application, called CovidKeyS, which uses DANKE to implement keyword search over three COVID-19 data scenarios.