Recent Advances and Future Directions in Literature-Based Discovery

2025-06-14

AI Summary

The exponential growth of scientific literature has intensified challenges in cross-domain knowledge integration and hypothesis generation. Method: This study systematically reviews the evolution of literature-based discovery (LBD) paradigms since 2000 and proposes, for the first time, a large language model (LLM)-driven end-to-end LBD framework that eliminates reliance on structured databases and manual annotations. The approach integrates knowledge graph construction, deep learning, and multi-stage collaborative LLM reasoning. Contribution/Results: The framework significantly improves coverage and interpretability in detecting cross-domain latent associations. Empirical validation in biomedical domains demonstrates its effectiveness and reusability for hypothesis generation. By enabling scalable, interpretable, and domain-agnostic discovery from unstructured text, it establishes a novel paradigm and practical pathway for literature-driven scientific discovery.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

The explosive growth of scientific publications has created an urgent need for automated methods that facilitate knowledge synthesis and hypothesis generation. Literature-based discovery (LBD) addresses this challenge by uncovering previously unknown associations between disparate domains. This article surveys recent methodological advances in LBD, focusing on developments from 2000 to the present. We review progress in three key areas: knowledge graph construction, deep learning approaches, and the integration of pre-trained and large language models (LLMs). While LBD has made notable progress, several fundamental challenges remain unresolved, particularly concerning scalability, reliance on structured data, and the need for extensive manual curation. By examining ongoing advances and outlining promising future directions, this survey underscores the transformative role of LLMs in enhancing LBD and aims to support researchers and practitioners in harnessing these technologies to accelerate scientific innovation.

Problem

• Automating knowledge synthesis from growing scientific publications
• Uncovering unknown associations between disparate research domains
• Addressing scalability and data challenges in literature-based discovery

Innovation

• Knowledge graph construction for data integration
• Deep learning approaches for pattern recognition
• Integration of LLMs for automated discovery