Knowledge Graph Extraction from Total Synthesis Documents
Keywords: : Knowledge Graph, LLM, Organic Synthesis, Benchmark
TL;DR: We introduce GOSyBench, a benchmark for knowledge graph extraction (KGE) from chemistry papers, and develop KGE algorithms with LLMs and VLMs. Our best KGE system reaches 73% accuracy, making GOSyBench a challenging benchmark.
Abstract: Knowledge graphs (KGs) have emerged as a powerful tool for organizing and integrating complex information, making it a suitable format for scientific knowledge. However, translating scientific knowledge into KGs is challenging as a wide variety of styles and elements to present data and ideas is used. Although efforts for KG extraction (KGE) from scientific documents exist, evaluation remains challenging and field-dependent; and existing benchmarks do not focuse on scientific information. Furthermore, establishing a general benchmark for this task is challenging as not all scientific knowledge has a ground-truth KG representation, making any benchmark prone to ambiguity. Here we propose Graph of Organic Synthesis Benchmark (GOSyBench), a benchmark for KG extraction from scientific documents in chemistry, that leverages the native KG-like structure of synthetic routes in organic chemistry. We develop KG-extraction algorithms based on LLMs (GPT-4, Claude, Mistral) and VLMs (GPT-4o), the best of which reaches 73% recovery accuracy and 59% precision, leaving a lot of room for improvement. We expect GOSyBench can serve as a valuable resource for evaluating and advancing KGE methods in the scientific domain, ultimately facilitating better organization, integration, and discovery of scientific knowledge.
Submission Number: 199
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