An entity alignment approach coupling NGBoost and SHAP for constructing spatio-temporal evolution knowledge graph from historical atlases

The historical atlases provide a wealth of information about the evolution of geography over time and space. The alignment of geographical entities across varying time periods is a crucial aspect of extracting meaningful insights into the spatio-temporal dynamics of geography. This paper proposes a geographic entity alignment approach coupling Natural Gradient Boosting (NGBoost) with SHapley Additive exPlanation (SHAP). Taking the historical atlas of China as a case study, a geographic entity alignment model based on NGBoost is constructed considering the different kinds of similarity features of geographic entities, including semantic, distance, shape, size and topology. The contribution of similarity features in the NGBoost model is analyzed using the SHAP framework so as to improve the explanatory capacity of the model. The spatio-temporal evolution relationships of geographic entities are generated by association rules depending on alignment types and represented as quadruples, for constructing geographic knowledge graphs. The proposed NGBoost method was found a superior accuracy by comparing with BP neural networks, random forests, and other alternative methods for aligning geographic entities. The constructed geographic spatio-temporal evolution knowledge graphs offer valuable support for the queries of evolutionary knowledge.