Automatic Construction Method of Legal Knowledge Graph Based on Part of Speech and Word Order Analysis
The paper constructed a legal Knowledge Graph through mining the entities and their relationships in the legal text,aiming to facilitate intelligent judgment and enhance the methodology of legal Knowledge Graph construction.This paper employs Natural Language Processing techniques rooted in LexNLP,analyzes the legal texts,and conducts sentence-level part-of-speech analysis,wherein nouns functioning as subjects or objects are labeled as entities,while verbs serving as predicates are labeled as relationships.Based on this framework,the entities and relationships within each sentence are permuted and combined according to the format<entity 1,relationship,entity 2>,resulting in the generation of non-repetitive knowledge triplets so as to generate a high-caliber legal knowledge graph.The paper proposes an automated construction approach for the legal Knowledge Graph based on part-of-speech and word order analyses,takes the legal precedents contained in the Caselaw Access Project in the US as the raw data,and assesses the quality of the generated triplets and presents a legal Knowledge Graph.
construction of Knowledge Graphentity recognitionrelation extractionNatural Language Processing
国家科技期刊平台
NSTL
万方数据
