KnowSite: Leveraging Urban Knowledge Graph for Site Selection

Abstract

Site selection determines optimal locations for new stores, which is of crucial importance for business success and urban development. Especially, the wide application of artificial intelligence with multi-source urban data makes intelligent site selection promising. Nevertheless, existing data-driven approaches heavily rely on feature engineering, which cannot take the complex relationships as well as the diverse influences of various semantics among data into consideration. Further, most approaches fail to reveal underlying factors for site decisions. To get rid of the dilemma, in this work, leveraging the knowledge graph (KG) technique, we propose a knowledge-driven model for site selection, short for KnowSite. Specifically, by empowering rich semantics in KG, we firstly construct an urban KG (UrbanKG) for site selection knowledge discovery with cities' key elements and complex relationships captured. Based on UrbanKG, we apply pre-training for semantic representations, and then design a generalized encoder-decoder structure for site decisions. KnowSite designs a graph neural network based encoder to adaptively model diverse influences, and further builds a relation path based decoder revealing the reasons behind site decisions. Extensive experiments on two datasets demonstrate that KnowSite outperforms representative baselines by more than 9% on precision. Moreover, KnowSite provides intuitive and convincing explanations for site decisions and sheds light on the site selection understanding.