Ranked neuro fuzzy inference system (RNFIS) for information retrieval

Authors:
Asif Nawaz;Aasia Khanum
Affiliations:
College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad, Pakistan;College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad, Pakistan
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Year:
2011

Citing 7
Cited 0

A mass assignment theory of the probability of fuzzy events

Fuzzy Sets and Systems
A vector space model for automatic indexing

Communications of the ACM
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence

Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Modern Information Retrieval

Modern Information Retrieval
Handling Vagueness in Information Retrieval Systems

ANNES '95 Proceedings of the 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems
Overview and semantic issues of text mining

ACM SIGMOD Record
Vagueness and uncertainty in information retrieval: how can fuzzy sets help?

Proceedings of the 2006 international workshop on Research issues in digital libraries

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper presents a novel approach to informational retrieval based on a synergy of knowledge-based models, set theoretic models, and vector space models of domain within a Fuzzy Logic framework. An input query is expanded to multiple synonym queries based on query semantics. Each document in the collection is divided into different zones with different relative importance assigned to each zone indicating its role in the query. Fuzzy rule bases are applied to each zone with parameters derived from vector space models and semantic query expansion. Fuzzy inference procedure outputs the relevance rank of each zone in satisfying the query. The relevance ranks of different zones are aggregated using the Ordered Weighted Averaging (OWA) operator to get the overall relevance rank of the complete document. The documents are ranked according to their relevance. The system has been tested on a standard dataset and has been demonstrated to show improved performance over typical vector space based approaches.