An Extended Fuzzy Linguistic Approach to Generalize Boolean Information Retrieval

The generalization of Boolean information retrieval systems is still of interest to scholars. In spite of the fact that commercial systems use Boolean retrieval mechanisms, such systems still have some limitations. One of the main problems is that such systems lack the ability to deal well with imprecision and subjectivity. Previous efforts have led to the introduction of numeric weights to improve both document representations (term weights) and query languages (query weights). However, the use of weights requires a clear knowledge of the semantics of the query in order to translate a fuzzy concept into a precise numeric value. Moreover, it is difficult to model the matching of queries to documents in a way that will preserve the semantics of user queries. A linguistic extension has been generated, starting from an existing Boolean weighted retrieval model and formalized within fuzzy set theory, in which numeric query weights are replaced by linguistic descriptors that specify the degree of importance of the terms. In the past, query weights were seen as measures of the importance of a specific term in representing the query or as a threshold to aid in matching a specific document to the query. The linguistic extension was originally modeled to view the query weights as a description of the ideal document, so that deviations would be rejected whether a given document had term weights that were too high or too low. This paper looks at an extension to the linguistic model that is not symmetric in that documents with a term weight below the query weight are treated differently than documents with a term weight above the query weight.