Text to Graph

Text-to-graph (T2G) research focuses on automatically converting unstructured textual information into structured graph representations, aiming to facilitate knowledge extraction, reasoning, and downstream tasks. Current efforts leverage large language models (LLMs) and graph neural networks (GNNs), often employing techniques like graph-to-tree encoding, self-supervised learning, and multi-task training to improve accuracy and efficiency, particularly in low-resource settings. This field is significant because it bridges the gap between human-readable text and machine-processable graph data, impacting diverse applications such as drug discovery, knowledge base construction, and document analysis. The development of standardized datasets and benchmarks is also a key area of focus, enabling more robust model evaluation and comparison.