关键词: 领域知识图谱, 信息抽取, 知识图谱构建, 模式约束, 提示工程

Abstract: Domain-specific Knowledge Graph (DKG) organize unstructured professional knowledge to enhance downstream tasks. However, existing construction methods often neglect task-specific requirements, leading to redundancy, and rely on costly expert-annotated schemas. To address this, an end-to-end DKG construction framework that eliminates manual schema input was proposed. By decomposing the process into schema induction and knowledge extraction, the framework leverages the LLM "Reasoning-Action-Reflection" paradigm to synchronize schema optimization with constrained extraction. This approach yields task-aligned triples and reusable schemas. Experimental results on datasets from the police and medical fields demonstrate that, without the need for manually predefined precise schema, the proposed method outperforms most baseline methods in terms of Jaccard, recall, and F1 scores, achieving performance close to that of methods constrained by manual schema. This proves the effectiveness of the method in constructing domain knowledge graphs at a low cost.

Key words: Domain-specific Knowledge Graph (DKG), information extraction, Knowledge Graph Construction (KGC), schema constraint, prompt engineering