A model of an information retrieval system with unbalanced fuzzy linguistic information

Abstract

Most information retrieval systems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express the weights of queries and the relevance degrees of documents. However, to improve the system-user interaction, it seems more adequate to express these linguistic weights and degrees by means of unbalanced linguistic scales, that is, linguistic term sets with different discrimination levels on both sides of the middle linguistic term. In this contribution we present an information retrieval system that accepts weighted queries whose weights are expressed using unbalanced linguistic term sets. Then, the system provides the retrieved documents classified in linguistic relevance classes assessed on unbalanced linguistic term sets. To do so, we propose a methodology to manage unbalanced linguistic information and we use the linguistic 2-tuple model as the representation base of the unbalanced linguistic information. Additionally, the linguistic 2-tuple model allows us to increase the number of relevance classes in the output and also to improve the performance of the information retrieval system.

Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information

“…To manage unbalanced fuzzy linguistic information, we propose a methodology similar to those proposed in [6,21,33]. This methodology is based on the transformation of the unbalanced fuzzy linguistic information in a Linguistic Hierarchy (LH) [28], which is the linguistic representation domain that allows us to develop comparison and combination processes of unbalanced fuzzy linguistic information.…”

“…Many of these problems use linguistic variables assessed in linguistic term sets whose terms are uniformly and symmetrically distributed, i.e., assuming the same discrimination levels on both sides of mid linguistic term. However, there exist problems that need to assess their variables with linguistic term sets that are not uniformly and symmetrically distributed [21,33]. This type of linguistic term sets are called unbalanced linguistic term sets (see Fig.…”

A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling

“…Similarly, usually users are interested in the relevant documents much more than in the non-relevant documents, and then, a best tuning of the output of the IRS can be achieved if a higher number of discrimination levels on the right of the middle linguistic term is assumed (see Figure 4). So, Enrique HerreraViedma and Antonio G. López-Herrera propose in 38 the first model of IRS based on an unbalanced FLM. This new unbalanced linguistic IRS accepts multiweighted queries whose weights are expressed using unbalanced linguistic term sets and interpreted according to an importance semantics and threshold semantics.…”

Multi-attribute group decision making methods with proportional 2-tuple linguistic assessments and weights

“…Martínez et al 32 applied the model with unbalanced linguistic terms to sensory evaluation. Herrera-Viedma and López-Herrera 28 developed a model of information retrieval system with unbalanced fuzzy linguistic information. Herrera-Viedma et al 27 and Meng and Pei 41 studied the linguistic aggregation operators with unbalanced linguistic information.…”