面向税收条例的知识图谱构建方法

Knowledge Graph Construction Method for Tax Regulations

税收条例是税法中的一类重要税务数据,是加强税收征收管理、规范税务征收和缴纳行为的法律依据。首先,针对税收条例知识复杂、专业名词多、逻辑结构明显等特点,定义面向税收条例的知识建模方法,提出将税收条例表示成条例知识子图,将子图中纳税人源节点进行融合,形成知识图谱表示;然后,针对税收条例文本特征设计要素抽取算法,提出基于BERT的税收条例知识要素抽取模型,相较于传统基于静态词向量的序列标注模型,P值、R值、F1值均大幅提升;最后,基于要素抽取模型实现面向税收条例知识图谱的自动构建,为智慧税务应用提供语义支撑。建立面向税务征收条例的知识图谱,对“智慧税务”应用推进具有重要现实意义。

Tax regulations are an important type of tax data in tax laws,serving as the legal basis for strengthening tax collection management and regulating tax collection and payment behavior.Firstly,in response to the complex knowledge,numerous professional terms,and obvious logical structure of tax regulations,a knowledge modeling method for tax regulations is defined.It is proposed to represent tax regulations as a sub graph of regulation knowledge,and fuse the taxpayer source nodes in the sub graph to form a knowledge graph representation;Then,a fea⁃ture extraction algorithm is designed for the text features of tax regulations,and a BERT based model for extracting knowledge elements of tax regulations is proposed.Compared to traditional sequence annotation models based on static word vectors,the P-value,R-value,and F1 are significantly improved;Finally,based on the feature extraction model,automatic construction of a knowledge graph for tax regulations is achieved,providing semantic support for smart tax applications.Establishing a knowledge graph for tax collection regulations is of great practi⁃cal significance for promoting the application of"smart taxation".