A model of fuzzy linguistic IRS based on multi-granular linguistic information

Abstract

An important question in IRSs is how to facilitate the IRS-user interaction, even more when the complexity of the fuzzy query language makes difficult to formulate user queries. The use of linguistic variables to represent the input and output information in the retrieval process of IRSs significantly improves the IRS-user interaction. In the activity of an IRS, there are aspects of different nature to be assessed, e.g., the relevance of documents, the importance of query terms, etc. Therefore, these aspects should be assessed with different uncertainty degrees, i.e., using several label sets with different granularity of uncertainty. In this contribution, an IRS based on fuzzy multi-granular linguistic information and a method to process the multi-granular linguistic information are proposed. The system accepts Boolean queries whose terms can be simultaneously weighted by means of ordinal linguistic values according to three semantics: a symmetrical threshold semantics, a relative importance semantics and a quantitative semantics. In the three semantics, the linguistic weights are represented by the linguistic variable “Importance”, but assessed on different label sets S 1, S 2 and S 3, respectively. The IRS evaluates weighted queries and obtains the linguistic retrieval status values of documents represented by the linguistic variable “Relevance” which is expressed on a different label set S ′.