Intelligent Scientific Literature Explorer using Machine Learning (ISLE)

• URL: http://arxiv.org/abs/2512.12760v1
• Date: Sun, 14 Dec 2025 16:54:24 GMT
• Title: Intelligent Scientific Literature Explorer using Machine Learning (ISLE)
• Authors: Sina Jani, Arman Heidari, Amirmohammad Anvari, Zahra Rahimi,
• Abstract summary: 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.<n>The proposed framework contributes a foundation for AI-assisted scientific discovery.
• Score: 0.797970449705065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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