城市地铁出行知识图谱嵌入表达的超参数选择

doi:10.12082/dqxxkx.2023.230054

地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (6): 1164-1175.doi: 10.12082/dqxxkx.2023.230054

• 专刊：地理时空知识图谱理论方法与应用 • 上一篇下一篇

城市地铁出行知识图谱嵌入表达的超参数选择

罗秋雨¹(), 乐阳¹^,², 谷岩岩¹^,^*()

1.深圳大学建筑与城市规划学院，深圳 518060
2.深圳市空间信息智能感知与服务重点实验室，深圳 518060

收稿日期:2023-02-10 修回日期:2023-04-19 出版日期:2023-06-25 发布日期:2023-06-02
通讯作者: *谷岩岩（1989— ），男，江苏淮安人，博士，研究方向为城市知识图谱构建。E-mail: yyg@whu.edu.cn
作者简介:罗秋雨（1997— ），女，四川达州人，硕士生，主要从事空间地理智能研究。E-mail: luoqiuyu2020@email.szu.edu.cn
基金资助:
国家自然科学基金项目(42171449);国家自然科学基金项目(42101464);测绘遥感信息工程国家重点实验室开放研究基金项目(21I03)

Hyperparameter Selection for Urban Metro Travel Knowledge Graph Embedding

LUO Qiuyu¹(), YUE Yang¹^,², GU Yanyan¹^,^*()

1. School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
2. Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen 518060, China

Received:2023-02-10 Revised:2023-04-19 Online:2023-06-25 Published:2023-06-02
Contact: *GU Yanyan, E-mail: yyg@whu.edu.cn
Supported by:
National Natural Science Foundation of China(42171449);National Natural Science Foundation of China(42101464);Open Research Fund Program of LIESMARS(21I03)

摘要/Abstract

摘要：

知识图谱作为人工智能技术与应用中重要的数据基础设施，已经成为地理科学领域的一个研究热点。目前对地理知识图谱进行嵌入表达时通常使用默认的超参数（如2层网络搜索深度），但是部分地理知识图谱的网络规模和拓扑特征与通用知识图谱不同，其合理性需进一步论证。为此，本文围绕城市轨道交通人地关系，基于地铁线路网络的拓扑结构特征，结合客流数据、POI（兴趣点）数据以及建成环境数据等构建地铁出行知识图谱；利用GraphSAGE模型学习实体的多维度特征嵌入，并结合POI数据对站点分类结果进行语义识别，对比验证适合地铁出行知识图谱嵌入表达的网络搜索深度。不同于默认的 2层搜索深度，当搜索深度为3层时，本研究所构建的地铁出行知识图谱的节点嵌入效果最优。因此，地理知识图谱嵌入表达的超参数选择需要顾及时空和人类活动相关的网络规模和拓扑特征，要避免不加甄别地使用其他领域通用知识图谱的已有成果。使用3层搜索深度获得的地铁站点分类结果也更具合理的解释性，可为利用知识图谱和人工智能方法进行站点规划和客流预测提供基础。

关键词: 知识图谱, 嵌入表达, 节点分类, 超参数, 搜索深度, GraphSAGE, 地铁客流, 城市出行

Abstract:

Knowledge graphs are an important data infrastructure in AI technologies and applications, and have become a hot research topic in geosciences. The size and topological features in geographic knowledge graphs are usually different from universal knowledge graphs, which are not typical small-world networks. However, existing studies often use the default network search depth when learning geographic knowledge graph representations, and its rationality needs further demonstration. For this purpose, this paper constructs a metro travel knowledge graph based on the topological structure features of metro line network, combined with passenger flow data, POI (Point of Interest) data and built environment data, etc.; then GraphSAGE model is used to learn node multidimensional feature embedding and combine POI data for semantic recognition of station classification results to verify the suitable network search depth for metro travel knowledge graph. The results showed that, compared to the default 2 layers search depth, the node embedding features of this metro travel knowledge graph work optimally when the search depth is 3 layers. This study shows that the hyperparameter selection of the geographic knowledge graph representation is supposed to take into account the geographic features, and it is important to avoid the use of results from fields such as computer science that have not been distinguished. When the search depth is 3 layers, the metro station classification results are also more reasonable and explanatory, which can provide a basis for station planning and passenger flow prediction using knowledge graph and AI methods.

Key words: knowledge graph, embedding, node classification, hyperparameter, search depth, GraphSAGE, metro passenger flow, urban travel

罗秋雨, 乐阳, 谷岩岩. 城市地铁出行知识图谱嵌入表达的超参数选择[J]. 地球信息科学学报, 2023, 25(6): 1164-1175.DOI:10.12082/dqxxkx.2023.230054

LUO Qiuyu, YUE Yang, GU Yanyan. Hyperparameter Selection for Urban Metro Travel Knowledge Graph Embedding[J]. Journal of Geo-information Science, 2023, 25(6): 1164-1175.DOI:10.12082/dqxxkx.2023.230054

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大类	中类
居住	第一类居住用地；第二类居住用地；第三类居住用地；第四类居住用地
公共服务	公共设施；科教文化；体育休闲；医疗保健；政府机构
商业	餐饮服务；购物服务；金融保险；汽车摩托；生活服务；住宿服务
办公	公司企业；商务写字楼
交通	道路附属；地址地名；交通设施
绿地与广场	风景名胜；公园广场

居住用地类型	内容
一类居住用地	独立式住宅（别墅），配套齐全，布局完整
二类居住用地	以多层、中高层为主，配套齐全，布局完整
三类居住用地	单身宿舍
四类居住用地	原农村居民住宅用地

ID	时间	线路	进出站	名称	经度/°E	纬度/°N
68580****	2018-11-05T20:49:29	地铁一号线	3（进站）	站A	114.****	22.****
68580****	2018-11-05T21:30:16	地铁四号线	4（出站）	站B	114.****	22.****

城市地铁出行知识图谱嵌入表达的超参数选择

Hyperparameter Selection for Urban Metro Travel Knowledge Graph Embedding

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