Ontologies are formal representations of knowledge in specific domains that provide a structured framework for organizing and understanding complex information. Creating ontologies, however, is a complex and time-consuming endeavor. ChEBI is a well-known ontology in the field of chemistry, which provides a comprehensive resource for defining chemical entities and their properties. However, it covers only a small fraction of the rapidly growing knowledge in chemistry and does not provide references to the scientific literature. To address this, we propose a methodology that involves augmenting existing annotated text corpora with knowledge from Chebi and fine-tuning a large language model (LLM) to recognize chemical entities and their roles in scientific text. Our experiments demonstrate the effectiveness of our approach. By combining ontological knowledge and the language understanding capabilities of LLMs, we achieve high precision and recall rates in identifying both the chemical entities and roles in scientific literature. Furthermore, we extract them from a set of 8,000 ChemRxiv articles, and apply a second LLM to create a knowledge graph (KG) of chemical entities and roles (CEAR), which provides complementary information to ChEBI, and can help to extend it.

本研究解决了现有化学本体（如ChEBI）覆盖范围有限且无引用文献的问题。我们提出了一种新的方法，通过结合知识库与大语言模型（LLM）的语言理解能力，自动识别科学文献中的化学实体及其角色，并构建了创新的知识图谱（CEAR），为现有本体提供了补充信息，具有重要的扩展潜力。

CEAR：从科学文献中自动构建化学实体及其角色的知识图谱