Knowledge-driven Site Selection via Urban Knowledge Graph
  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.
