Implementation of an efficient Fuzzy Logic based Information Retrieval System

TL;DR

This paper presents an efficient fuzzy logic-based information retrieval system that improves document relevance scoring and outperforms traditional cosine similarity methods, demonstrated on TREC datasets.

Contribution

The paper introduces a fuzzy logic-based similarity model for IR systems, showing enhanced performance over cosine similarity in retrieval accuracy.

Findings

Fuzzy similarity model outperforms cosine similarity in precision-recall metrics.

The system effectively computes document-query relevance using fuzzy logic.

Experimental results confirm the dominance of fuzzy similarity in IR accuracy.

Abstract

This paper exemplifies the implementation of an efficient Information Retrieval (IR) System to compute the similarity between a dataset and a query using Fuzzy Logic. TREC dataset has been used for the same purpose. The dataset is parsed to generate keywords index which is used for the similarity comparison with the user query. Each query is assigned a score value based on its fuzzy similarity with the index keywords. The relevant documents are retrieved based on the score value. The performance and accuracy of the proposed fuzzy similarity model is compared with Cosine similarity model using Precision-Recall curves. The results prove the dominance of Fuzzy Similarity based IR system.