Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding

Abstract

Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical significance of results. To explore the network structure and semantic relationships, we propose a general framework for multi-source spatio-temporal data analysis via knowledge graph embedding. The framework extracts low-dimensional feature representation from multi-source spatio-temporal data in a high-dimensional space, and recognizes the network structure and semantic relationships about multi-source spatio-temporal data. Experiment results show that the framework can not only effectively utilize multi-source spatio-temporal data, but also explore the network structure and semantic relationship. Taking real Shanghai datasets as an example, we confirm the validity of the multi-source spatio-temporal data analytical framework based on knowledge graph embedding.

A Semantic-Spatial Aware Data Conflation Approach for Place Knowledge Graphs

“…However, the efficacy of these models in addressing the domain-specific needs of place entities matching remains unverified. Place entity matching differs significantly from general domain entity matching, primarily due to its emphasis on spatial characteristics [20]. A critical area of investigation is integrating spatial feature spaces with textual feature spaces within the model.…”