GeoCaseRec: a knowledge graph semantics-driven framework for sustainable urban renewal recommendation

However, this remains constrained by fragmented multi-source geo-referenced data, semantic heterogeneity, and limited case evaluation. To address these challenges, this study proposes GeoCaseRec, a knowledge graph semantics-driven framework for urban renewal recommendation.

Grounded in geographical case-based reasoning, the framework constructs an integrated representation model across four dimensions: geographical location, site conditions, spatial topology, and evolutionary characteristics. It further introduces a joint extraction method that combines large language models and spatial computation to derive key information from heterogeneous sources. Based on these data, intra-case spatial topologies and inter-case semantic hierarchies are constructed to automate an urban renewal knowledge graph.