Abstract
According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from bi orxiv.org: "Knowledge graphs have recently emerged as a powerful data structure to organize biomedical knowledge with explicit representation of nodes and edges. The knowl edge representation is in a machine-learning ready format and supports explainab le AI models. "However, PubMed, the largest and richest biomedical knowledge repository, exist s as free text, limiting its utility for advanced machine learning tasks. "To address the limitation, we present LiteralGraph, a computational framework t hat rigorously extracts biomedical terms and relationships from PubMed literatur e.
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