Intelligent Information Retrieval Using Deep Learning in Multi-Domain Data

Abstract

Intelligent information retrieval has emerged as an important requirement to be able to extract actionable knowledge out of large, heterogeneous and continuously changing data sources across a variety of areas. The paper proposed a smart information retrieval system that used the deep learning method to overcome the weaknesses of the conventional keyword-based and rule-based information retrieval systems in multi-domain settings. The paper examined the theoretical basis of the classical and semantic information retrieval models, and how they fail to be able to effectively represent the context, field semantics, and cross-domain links. To address these, deep neural networks, transformer-based language models as well as the semantic matching mechanisms were combined into a domain-aware retrieval architecture. The noise filtering, missing value treatment, text tokenization, normalization, schema alignment, semantic standardization, and domain-sensitive feature extraction components are a part of the preprocessing pipeline to provide consistent and quality representation of input across the heterogeneous sources of data. Extensive performance assessment indicated that the retrieval accuracy, relevance ranking, and cross-domain generalization were better when using the traditional information retrieval methods. The proposed framework presents a domain aware modular architecture that has a semantic alignment across domains, which enhances robustness and generalization in heterogeneous data setting. Moreover, the review has emphasized the scalability of the framework, the efficiency of computation and the viability of the application of the framework in enterprise, healthcare, scientific and multimedia retrieval tasks.