Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs最新文献

Towards a representation of uncertain geospatial information in knowledge graphs 知识图谱中不确定地理空间信息的表示

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs Pub Date : 2022-11-01 DOI: 10.1145/3557990.3567588

L. Cadorel, A. Tettamanzi, Fabien L. Gandon

引用次数: 1

Finding map feature correspondences in heterogeneous geospatial datasets 在异构地理空间数据集中寻找地图特征对应关系

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs Pub Date : 2022-11-01 DOI: 10.1145/3557990.3567590

Abhilshit Soni, Sanjay Boddhu

{"title":"Finding map feature correspondences in heterogeneous geospatial datasets","authors":"Abhilshit Soni, Sanjay Boddhu","doi":"10.1145/3557990.3567590","DOIUrl":"https://doi.org/10.1145/3557990.3567590","url":null,"abstract":"In an automated map making process, map features like lane-markings, traffic-signs, poles, stop-lines and similar other features are extracted using deep learning methods from various sources of imagery or sensor data. These sources come with their own positional errors due to which the map features extracted from these sources are always misaligned with respect to each other, making the conflation of map features a difficult task. We propose a novel method to find map feature correspondences between 2 sets of map feature datasets obtained from different sources by first converting them into a heterogeneous geospatial graph and then doing node representation learning using a graph neural network that can generate vector embeddings that encode information of morphology, attributes, and absolute and relative positions of the map feature with respect to its neighbours along with aggregated information from its neighbours. This process can be employed to generate embeddings of map feature nodes, which are amicable to identifying spatially similar and corresponding map feature nodes across disparate sources with varying degree of similarity scores. When applied aptly, these map feature correspondences between two sources can be used as anchor points to perform spatial alignment with linear or nonlinear transforms, leading to a better conflation.","PeriodicalId":117618,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126133229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Developing knowledge graph based system for urban computing 基于知识图谱的城市计算系统开发

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs Pub Date : 2022-11-01 DOI: 10.1145/3557990.3567586

Yu Liu, Jingtao Ding, Yong Li

{"title":"Developing knowledge graph based system for urban computing","authors":"Yu Liu, Jingtao Ding, Yong Li","doi":"10.1145/3557990.3567586","DOIUrl":"https://doi.org/10.1145/3557990.3567586","url":null,"abstract":"Everyday our living city produces a tremendous amount of spatial-temporal data, involved with multiple sources from the individual scale to the city scale. Undoubtedly, such massive urban data can be explored for a better city and better life, as what the urban computing community has been dedicating in recent years. Nevertheless, existing studies are still facing the challenges of data fusion for the urban data as well as the knowledge distillation for specific applications. Moreover, there is a lack of full-featured and user-friendly platform for both researchers and developers in urban computing scenario. Therefore, in this paper, we present an urban knowledge graph (UrbanKG) system to incorporate knowledge graph with urban computing. Specifically, the system introduces a complete scheme to construct knowledge graph for urban data fusion from Data layer to Construction layer. The system further develops the multiple layers of Storage, Algorithm, Operation and Applications, which achieve the knowledge distillation and support various functions to the users. We perform three representative and practical use cases and demonstrate the system capability of boosting performance in various downstream applications, indicating a promising research direction of knowledge-driven urban computing.","PeriodicalId":117618,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115457926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Measuring network resilience via geospatial knowledge graph: a case study of the us multi-commodity flow network 地理空间知识图谱测量网络弹性:以美国多商品流动网络为例

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs Pub Date : 2022-10-09 DOI: 10.1145/3557990.3567569

Jinmeng Rao, Song Gao, Michelle Miller, Alfonso Morales

引用次数: 4

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs 第一届ACM SIGSPATIAL国际地理空间知识图研讨会论文集

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs Pub Date : 1900-01-01 DOI: 10.1145/3557990

引用次数: 0