Although all databases frameworks let us make conventional (crisp) searches, there are only a few of them that take into account some flexible, fuzzy, expressive criteria. The few of them that provide some of these searching characteristics are restricted to a particular database format, as FleSe that is devoted to search in a flexible way over Prolog databases. We have extended FleSe framework by an extraordinary feature that allows users to query various conventional and modern databases such as Prolog, CSV, XLS, XLSX, MySQL, and MongoDB or JSON in a fuzzy way. We have developed an adaptable and configurable platform for it so that any user can personalize at runtime. The fuzzy searching criteria can be created and added in a very userfriendly way, so that any user can upload his/her conventional (crisp) database, define the fuzzy search criteria that he/she is interested in and search at the database flexibly and expressively using concepts as similarity, fuzziness, qualification, and negation.
We present a framework that allows any user (without the need of neither technical no theoretical knowledge) to define fuzzy criteria based on the non-fuzzy information stored in databases in an easy way. The interests for developing such a framework is to provide a human-oriented (fuzzy and nonfuzzy) search engine with a user-friendly interface to perform expressive and flexible searches over databases. We achieved this task by providing an intelligent interface for the users to define fuzzy criteria without having any knowledge about its low-level syntax or implementation details. Our framework allows users to pose different queries (combining crisp and fuzzy search criteria) over various conventional and modern data formats such as JSON, SQL, Prolog, CSV, XLS and XLSX. We believe our approach adds to the advancement for more intelligent and human-oriented fuzzy search engines.
A bi-valued logic is not enough to make an intelligent search engine to give us the result for the queries like "I am looking for a cheap restaurant, Mediterranean food or similar type." With the integration of Fuzzy Logic and Logic Programming, we were able to model and pose flexible queries over databases. Therefore, we present a framework that allows users to pose their expressive queries based on defining similar relation criteria over various modern and conventional data formats such as JSON, SQL, CSV, XLS, and XLSX. The interest is in, for example, obtaining "drama movie" when asking for "romantic movie" (only if the similarity relation between drama and romantic movie is explicitly defined in the configuration file). The uses of similarity relation between values allow us to obtain more answers apart from the identical one. The searches that use two or more criteria are much more expressive and accurate. This framework provides the facility to define, modify and remove similarity relations from a user-friendly interface (without the need to be concern about the low-level syntax of the similarity criteria).
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