The Application of Fuzzy Logic to the Construction of the Ranking Function of Information Retrieval Systems

TL;DR

This paper introduces a fuzzy logic approach to constructing ranking functions in information retrieval systems, enhancing interpretability and flexibility while maintaining competitive performance with established search engines.

Contribution

It presents a novel fuzzy logic-based method for defining ranking functions, enabling natural language rule conversion and integration with vector models.

Findings

Achieved performance comparable to Apache Lucene

Fuzzy rules improve interpretability of ranking functions

Combines logic-based and vector models effectively

Abstract

The quality of the ranking function is an important factor that determines the quality of the Information Retrieval system. Each document is assigned a score by the ranking function; the score indicates the likelihood of relevance of the document given a query. In the vector space model, the ranking function is defined by a mathematic expression. We propose a fuzzy logic (FL) approach to defining the ranking function. FL provides a convenient way of converting knowledge expressed in a natural language into fuzzy logic rules. The resulting ranking function could be easily viewed, extended, and verified: * if (tf is high) and (idf is high) > (relevance is high); * if (overlap is high) > (relevance is high). By using above FL rules, we are able to achieve performance approximately equal to the state of the art search engine Apache Lucene (deltaP10 +0.92%; deltaMAP -0.1%). The fuzzy logic approach allows combining the logic-based model with the vector model. The resulting model possesses simplicity and formalism of the logic based model, and the flexibility and performance of the vector model.