Information retrieval in big data using cognitive approaches

TL;DR

This paper introduces a topology-based cognitive approach for effective information retrieval in big data environments, emphasizing semantic alignment and logical connectives to improve retrieval accuracy.

Contribution

It proposes a novel topology-based cognitive information retrieval method that incorporates semantic similarity and logical connectives for big data applications.

Findings

Enhanced semantic alignment between queries and documents

Incorporation of logical connectives improves retrieval relevance

Applicable to large-scale data environments

Abstract

Due to the exponential growth of big data in this digital era, an advanced method for effective information retrieval becomes essential. The basic objective of this paper is to propose a topology-based method for cognitive information retrieval (CIR) in big data environments. By using concepts such as cognitive similarity distances, metric spaces, retrieval topologies, etc., this paper aims to propose the semantic alignment between user queries and document repositories. The paper also extends this approach to incorporate logical connectives in cognitive information retrieval.