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Site selection determines optimal locations for new stores, which is ofcrucial importance to business success. Especially, the wide application ofartificial intelligence with multi-source urban data makes intelligent siteselection promising. However, existing data-driven methods heavily rely onfeature engineering, facing the issues of business generalization and complexrelationship modeling. To get rid of the dilemma, in this work, we borrow ideasfrom knowledge graph (KG), and propose a knowledge-driven model for siteselection, short for KnowSite. Specifically, motivated by distilled knowledgeand rich semantics in KG, we firstly construct an urban KG (UrbanKG) withcities' key elements and semantic relationships captured. Based on UrbanKG, weemploy pre-training techniques for semantic representations, which are fed intoan encoder-decoder structure for site decisions. With multi-relational messagepassing and relation path-based attention mechanism developed, KnowSitesuccessfully reveals the relationship between various businesses and siteselection criteria. Extensive experiments on two datasets demonstrate thatKnowSite outperforms representative baselines with both effectiveness andexplainability achieved.

Knowledge-driven Site Selection via Urban Knowledge Graph