Competition among tourism enterprises is an ineluctable component of sustainable tourism growth, requiring comprehensive studies to understand its dynamic and develop appropriate strategies. The literature employs text mining or statistical analyses to identify correlations between tourism areas as competitive relationships. However, this approach may not be fully applicable, due to the sparsity of crucial coexistence phenomena, and may fail to investigate fine‐grained attractions' competition inside destination using large‐scale geospatial data. To overcome the limitations, this study proposes a knowledge‐driven competitive intelligence framework for tourism management, utilizing knowledge graph (KG) construction and inference technologies. First, multi‐mode heterogeneous tourism data are integrated into a unified KG, including tourist check‐in, online text, and basic geographic information. Second, the spatial‐dependent GNN‐based model absorbing abundant spatial semantic knowledge from tourism‐oriented KG can enhance the performance of competition reasoning. Third, with multiple analyses via symbolic queries on KG, a comprehensive panorama of competition situations can be revealed.

中文翻译：

---

知识驱动的旅游空间竞争情报

旅游企业之间的竞争是旅游业可持续增长不可避免的组成部分，需要进行全面研究以了解其动态并制定适当的战略。文献采用文本挖掘或统计分析来将旅游地区之间的相关性识别为竞争关系。然而，由于关键共存现象的稀疏性，这种方法可能并不完全适用，并且可能无法使用大规模地理空间数据来研究目的地内部细粒度景点的竞争。为了克服这些局限性，本研究利用知识图（KG）构建和推理技术，提出了一种知识驱动的旅游管理竞争情报框架。首先，将多模式异构旅游数据整合到一个统一的知识图谱中，包括游客签到、在线文本和基本地理信息。其次，基于空间依赖的 GNN 模型从面向旅游的 KG 中吸收丰富的空间语义知识，可以提高竞争推理的性能。第三，通过对KG进行符号查询的多重分析，可以全面了解比赛情况。