Knowledge-driven Site Selection via Urban Knowledge Graph
This addresses site selection for businesses, offering improved performance and explainability, but it is incremental as it builds on existing knowledge graph and pre-training techniques.
The paper tackles the problem of intelligent site selection for new stores by proposing a knowledge-driven model called KnowSite, which constructs an urban knowledge graph and uses pre-training and an encoder-decoder structure to outperform existing baselines in experiments on two datasets.
Site selection determines optimal locations for new stores, which is of crucial importance to business success. Especially, the wide application of artificial intelligence with multi-source urban data makes intelligent site selection promising. However, existing data-driven methods heavily rely on feature engineering, facing the issues of business generalization and complex relationship modeling. To get rid of the dilemma, in this work, we borrow ideas from knowledge graph (KG), and propose a knowledge-driven model for site selection, short for KnowSite. Specifically, motivated by distilled knowledge and rich semantics in KG, we firstly construct an urban KG (UrbanKG) with cities' key elements and semantic relationships captured. Based on UrbanKG, we employ pre-training techniques for semantic representations, which are fed into an encoder-decoder structure for site decisions. With multi-relational message passing and relation path-based attention mechanism developed, KnowSite successfully reveals the relationship between various businesses and site selection criteria. Extensive experiments on two datasets demonstrate that KnowSite outperforms representative baselines with both effectiveness and explainability achieved.