SemGovNet-KG: A semantic communication network for government affair appeals empowered by knowledge graph

Abstract

With the continuous progress of society and the rapid development of information technology, the number of appeals on major government platforms is increasing rapidly, making traditional information processing methods difficult to efficiently handle and assign appeal information to relevant departments. To enhance government appeal handling efficiency, this paper proposes a semantic cognition architecture for government appeal classification, named SemGovNet-KG. This architecture constructs a domain-specific knowledge graph based on government information and designs a NeoBert-TextCNN based text classification model to achieve efficient semantic understanding and intelligent classification of government appeals. Addressing issues include semantic ambiguity, information silos, and weak knowledge associations in traditional government appeal processing, SemGovNet-KG integrates deep semantic modeling and knowledge graph reasoning mechanisms to build multi-level semantic representations and entity relationship networks. Experimental results show that SemGovNet-KG significantly improves classification accuracy, semantic interpretability, and system robustness on real government appeal datasets. This method provides a theoretical foundation and technical support for intelligent government services and efficient appeal response, with promising prospects for practical application.