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Research on Thematic Map Information Recommendation System Based on Knowledge Graph
编号：69 访问权限：仅限参会人 更新：2025-12-29 14:09:49 浏览：105次 拓展类型2

报告开始：2025年12月30日 16:15（Asia/Amman）

报告时间：15min

所在会场：[S5] Track 5: Emerging Trends of AI/ML [S5-2] Track 5: Emerging Trends of AI/ML

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摘要
With the growing application of Geographic Information Systems (GIS) in urban management, emergency response, and public services, traditional layer-based thematic map information services—relying on keyword retrieval and layer superposition—can no longer meet users’needs for semantic understanding and intelligent recommendation in cartography. To address this gap, this study proposes a knowledge graph (KG)-driven geographic information recommendation system framework. First, a geographic KG for thematic maps is constructed, focusing on geographic entities and their semantic relationships. High-quality entity relation extraction is achieved using the BERT+BiLSTM+CRF model, while graph embedding representation is implemented via random walk and word2vec to enable high-dimensional matching between user interest vectors and geographic information node vectors. On this basis, a hybrid recommendation model integrating KG semantic reasoning and graph embedding algorithms is designed, and a system prototype with recommendation, visualization, and feedback optimization capabilities is developed. Experimental results demonstrate that the system outperforms traditional methods in both recommendation accuracy(Precision@10: 0.78 vs. 0.58–0.67 for traditional methods) and processing speed(average response time: 250 ms vs. 370–410 ms for traditional methods), with strong practicality and scalability. This research achieves innovative breakthroughs in knowledge extraction, hybrid recommendation strategies, and system performance optimization, providing effective support for semantic scenario-oriented geographic information services.
 
关键词
Intelligent Service,Knowledge Graph, Thematic Map,Geographic Information Recommendation, Graph Embedding
报告人
Guoqiang Wang
Master's student Yulin University

稿件作者
Guoqiang Wang Yulin University
Feng Zhang Yulin University
Haoyan You Shaanxi Fundamental Geographic Information Center
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重要日期

  • 会议日期

    12月29日

    2025

    至

    12月31日

    2025

  • 12月30日 2025

    报告提交截止日期

  • 02月10日 2026

    初稿截稿日期

  • 02月10日 2026

    注册截止日期

主办单位
国际科学联合会
承办单位
扎尔卡大学
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