Semantic communication is emerging as a key paradigm for 6G networks, where the goal is not to perfectly reconstruct bits but to preserve the meaning that matters for a given task. This shift can improve bandwidth efficiency, robustness, and application-level performance. However, most existing studies focus solely on encoder-decoder design and ignore network-wide decision-making. As data traverses multiple hops, semantic relevance may decrease, routing may overlook meaningful information, and semantic distortion can increase under dynamic network conditions. To address these challenges, this paper proposes a management-oriented semantic communication framework built upon Knowledge-Defined Networking (KDN). The framework comprises three core modules: a semantic-reasoning module that computes relevance scores by mapping semantic embeddings onto a knowledge graph that encodes task concepts and contextual relationships; a semantic-aware routing mechanism that forwards data along paths that preserve meaning; and a semantic-distortion controller that adaptively adjusts encoding and routing to preserve semantic fidelity. Our ns-3 results show clear benefits: semantic delivery success improves by 12%, semantic distortion decreases by 22%, re-routing events drop by 44%, and throughput efficiency rises by 14% compared to baseline methods (shortest-path, load-based, and distortion-only routing). These results indicate that meaning-aware and feedback-driven control is essential for reliable and scalable semantic communication in future 6G networks.

翻译：语义通信正成为6G网络的关键范式，其目标并非精确重建比特流，而是保留对特定任务至关重要的意义信息。这一转变能够提升带宽效率、鲁棒性及应用层性能。然而，现有研究大多仅关注编解码器设计，忽视了网络层面的决策机制。当数据穿越多跳网络时，语义关联度可能降低，路由过程可能忽略关键语义信息，且在动态网络环境下语义失真会加剧。为应对这些挑战，本文提出一种基于知识定义网络（KDN）的面向管理的语义通信框架。该框架包含三个核心模块：语义推理模块——通过将语义嵌入映射至编码任务概念及上下文关系的知识图谱来计算关联度评分；语义感知路由机制——沿能保持语义完整性的路径转发数据；语义失真控制器——自适应调整编码与路由策略以维持语义保真度。基于ns-3的仿真结果表明：相较于基准方法（最短路径、基于负载及仅考虑失真的路由），本框架使语义传输成功率提升12%，语义失真降低22%，重路由事件减少44%，吞吐效率提高14%。这些结果证明，面向语义感知且基于反馈的控制机制对于未来6G网络中可靠、可扩展的语义通信至关重要。