Formalization for Natural Language Fuzzy Queries and Crisp Multi-Criteria Queries

It is common in real life to find fuzzy information that comes from subjective judgments or the imprecision in measured data. Fuzzy approaches have been used to extend database systems in storing and updating imprecise information (data) and in processing imprecise queries. Consider a fuzzy query: find name, grade of quite good students and just tall students where age > 15. This query includes two fuzzy concepts: good student and tall student and one crisp query criteria (i.e. age > 15). In this paper we present a formalization to process natural language fuzzy (expressive) queries and to return fuzzy results for crisp query criteria. Our formalization is general that can be particularized for implementation in variety of database platforms i.e. fuzzy web search, information systems supporting fuzzy data etc. Our approach only makes the fuzzy query writing much simpler and easier than conventional query writing but also close to human like thinking due to its true fuzzy nature. We also provide an operational semantics for fuzzy query processing which can be followed for multiple data types i.e. numeric, text, graphics etc. Our approach supports fuzzy querying for not only fuzzy data but also for missing data; hence enabling us to get query results closer to human thinking and expectations. It is an expressive model that let to make human-like (i.e. fuzzy) consults. Key–Words: Fuzzy Databases, Fuzzy Queries, Fuzzy Logic