Fuzzy retrieval system employing fuzzy connectives with learning functions and query networks

A new fuzzy connective and network structure for queries which are constructed by fuzzy connectives are proposed to overcome a drawback of conventional fuzzy retrieval systems. In a conventional fuzzy retrieval system, it is quite difficult for a user to obtain the most suitable results since the user cannot start with making up complete queries. In our retrieval system, if a user gives an estimation of fitting samples in a database which fit the user´s requests, AND/OR operators in queries which are made up of fuzzy connectives are adjusted to represent the user´s requests. With the adjusted parameters and a network for the query, this fuzzy retrieval system gives results which better satisfy the user´s requests. The consistencies of samples are also discussed. Inconsistent samples are defined, and an extracting method for inconsistent samples is proposed. The effectiveness of this proposed fuzzy retrieval system is shown through an experiment