Recent Advances and Future Directions in Literature-Based Discovery
It provides a comprehensive overview for researchers and practitioners in LBD, though it is incremental as a survey article.
This survey addresses the challenge of synthesizing knowledge from the growing volume of scientific publications by reviewing recent advances in literature-based discovery (LBD) from 2000 onward, highlighting progress in areas like knowledge graphs and LLMs, but notes unresolved issues such as scalability and manual curation.
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.