用于增强城市道路网络语义探索的地图—图谱交互模型

doi:10.13474/j.cnki.11-2246.2026.0617

摘要/Abstract

摘要： [目的] 针对网络地图在城市道路语义方面表达不足、空间与语义割裂的问题,本文提出了一种地图—图谱交互模型,以增强语义探索与认知推理能力。[方法]通过建立几何空间与语义空间的双向映射机制构建地图—图谱交互模型,并集成大语言模型构建自然语言问答接口,实现“空间-语义”的闭环交互。[结果]在原型系统上有效实现了空间与语义的双向交互,以及自然语言形式的道路知识查询与可视化。[结论]该模型有效弥合了道路空间表示与道路语义知识间的鸿沟,不仅提升了道路信息传输效率,还通过自然语言交互降低了理解复杂道路语义的认知门槛,为未来城市规划与空间智能决策提供了有效支持。

关键词: 网络地图, 城市道路语义, 知识图谱, 双向交互

Abstract: [Purposes] To address the inadequacies in expressing urban road semantics and the disconnect between spatial and semantic information in online maps,we propose a map-graph interaction model to enhance semantic exploration and cognitive reasoning capabilities.[Methods]By establishing a bidirectional mapping mechanism between geometric space and semantic space,we construct a map-graph interaction model.Integrating large language models,we build a natural language question-answering interface to achieve closed-loop “spatial-semantic” interaction.[Findings] The prototype system effectively implements bidirectional interaction between spatial and semantic elements,enabling natural language-based road knowledge queries and visualization.[Conclusions] This model effectively bridges the gap between road spatial representation and road semantic knowledge.It not only enhances the efficiency of road information transmission but also lowers the cognitive threshold for understanding complex road semantics through natural language interaction,providing robust support for future urban planning and spatial intelligence decision-making.

Key words: web map, urban road semantics, knowledge graph, bidirectional interaction

中图分类号:

P208

尹章才, 张政, 陈毅然. 用于增强城市道路网络语义探索的地图—图谱交互模型[J]. 测绘通报, 2026, 0(6): 112-118.

YIN Zhangcai, ZHANG Zheng, CHEN Yiran. Map-graph interaction model for enhancing semantic exploration of urban road networks[J]. Bulletin of Surveying and Mapping, 2026, 0(6): 112-118.

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链接本文: http://tb.chinasmp.com/CN/10.13474/j.cnki.11-2246.2026.0617

http://tb.chinasmp.com/CN/Y2026/V0/I6/112

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