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I. Muslea, “Machine Learning for Online Query Relaxation,” 10th International Conference of Knowledge and Discovery and Data Mining, Washington DC, 2004, pp. 246-255.

has been cited by the following article:

• TITLE: Dealing with Empty and Overabundant Answers to Flexible Queries

  AUTHORS: Samyr Abrahão Moises, Silvio do Lago Pereira

  KEYWORDS: Relational Database; Fuzzy Logic; Flexible Query; Empty Answer; Overabundant Answer

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.2 No.1, February 26, 2014

  ABSTRACT: In traditional database applications, queries intend to retrieve data satisfying precise conditions. As a result, thousands of data can be retrieved (overabundant answer) or, even worse, no data at all (empty answer). In both cases, the queries must be reformulated to produce more significant results and, typically, many related queries are submitted by a user before he can be finally satisfied. To overcome these problems, this paper proposes a unified solution in the framework of flexible queries with fuzzy semantics. This solution, based on the concept of semantic proximity and implemented in a tool for flexible query answering, allows the automatic reformulation of queries with empty or overabundant answers.