Predicting implicit concept embeddings for singular relationship discovery replication of closed literature-based discovery.

ABSTRACT:

Objective

Literature-based Discovery (LBD) identifies new knowledge by leveraging existing literature. It exploits interconnecting implicit relationships to build bridges between isolated sets of non-interacting literatures. It has been used to facilitate drug repurposing, new drug discovery, and study adverse event reactions. Within the last decade, LBD systems have transitioned from using statistical methods to exploring deep learning (DL) to analyze semantic spaces between non-interacting literatures. Recent works explore knowledge graphs (KG) to represent explicit relationships. These works envision LBD as a knowledge graph completion (KGC) task and use DL to generate implicit relationships. However, these systems require the researcher to have domain-expert knowledge when subm

SUBMITTER: Cuffy C 

PROVIDER: S-EPMC11920161 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature