Intelligent Scientific Literature Explorer using Machine Learning (ISLE)

TL;DR

This paper introduces an integrated AI system for scientific literature exploration that combines data acquisition, hybrid retrieval, semantic topic modeling, and knowledge graph construction to enhance discovery and understanding.

Contribution

It presents a comprehensive framework that unifies retrieval, topic modeling, and knowledge graph construction for improved scientific literature exploration.

Findings

Enhanced retrieval relevance demonstrated across multiple queries.

Improved topic coherence and interpretability.

Effective integration of heterogeneous data sources.

Abstract

The rapid acceleration of scientific publishing has created substantial challenges for researchers attempting to discover, contextualize, and interpret relevant literature. Traditional keyword-based search systems provide limited semantic understanding, while existing AI-driven tools typically focus on isolated tasks such as retrieval, clustering, or bibliometric visualization. This paper presents an integrated system for scientific literature exploration that combines large-scale data acquisition, hybrid retrieval, semantic topic modeling, and heterogeneous knowledge graph construction. The system builds a comprehensive corpus by merging full-text data from arXiv with structured metadata from OpenAlex. A hybrid retrieval architecture fuses BM25 lexical search with embedding-based semantic search using Reciprocal Rank Fusion. Topic modeling is performed on retrieved results using BERTopic or non-negative matrix factorization depending on computational resources. A knowledge graph unifies papers, authors, institutions, countries, and extracted topics into an interpretable structure. The system provides a multi-layered exploration environment that reveals not only relevant publications but also the conceptual and relational landscape surrounding a query. Evaluation across multiple queries demonstrates improvements in retrieval relevance, topic coherence, and interpretability. The proposed framework contributes an extensible foundation for AI-assisted scientific discovery.