一种多层政务知识图谱构建方法及示例

A Method and Example of Constructing Multi-layer Government Knowledge Graph

随着政务数字化建设的不断完善,基于知识图谱实现政务服务“知识化、个性化、智能化”的业务需求被逐渐唤醒。目前,政务领域知识图谱应用场景单一,与不同场景政务知识难以建立联系,基于传统数据库的政务服务搜索、办理和审批效率不高。为拓展政务服务场景,提升政务服务的搜索、办理和审批效率,该文提出一套自顶向下映射的多层政务知识图谱构建方法。首先,从政务服务角度出发构建政务知识概念模型;然后,依据概念模型获取政务知识、数据预处理和知识融合;最后,形成以概念、业务服务、社会服务和信息共享的自上而下关系的多层政务知识图谱。基于Neo4j可视化展示和已应用部署的服务,以房产审批的搜索、占用林地审批的办理和面向公众投诉的社会服务为例,对所提方法进行验证。实验结果表明,该方法不仅可以为不同政务场景提供知识图谱支撑和建立关联,还有助于实现多源政务数据融合共享,可为后续政务知识图谱构建提供图谱库参考。

With the continuous improvement of digital construction of government affairs,the need for realizing“knowledge-based,personalized and intelligent”government services based on knowledge graph is gradually being awakened.At present,the application of knowledge graph in the government domain is often oriented to a single scenario,making it difficult to establish connections between different scenarios with respect to government knowledge.The search,management and approval efficiency of government services based on traditional databases is still not high.To expand the scope of government services and improve the efficiency of search,management,examination and approval,this paper proposes a set of top-down mapping methods to construct a multi-layer government knowledge graph.Specifically,this method first construct the conceptual model of government knowledge from the perspective of government service,and then obtain the government knowledge,data preprocessing and knowledge fusion according to the conceptual model;finally,forming a multi-layer government knowledge graph with top-down relationships of concept,business service,social service and information sharing.With the visual display of Neo4j and the deployed services,paper validate the proposed method by taking the search of real estate approval,the examination and approval of occupied forest land and the social service for public complaints as examples,which proves that the method is efficient and feasible.It not only provides knowledge graph support and association for different government scenarios,but also helps to realize the fusion and sharing of multi-source government data,thus providing a reference for the subsequent construction of government knowledge graph.