[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4107":3,"related-4107":73},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":37,"slug":38,"view_count":39,"doi":40,"paper":41,"created_at":72},4107,"GeoCaseRec: a knowledge graph semantics-driven framework for sustainable urban renewal recommendation","https:\u002F\u002Fdoi.org\u002F10.1080\u002F17538947.2026.2738331","As urbanisation shifts from outward expansion to the regeneration of existing built environments, sustainable urban renewal increasingly depends on the effective reuse of historical cases. 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. To bridge the technical gap in the recommendation phase, GeoCaseRec employs a Heterogeneous Graph Neural Network to fuse multi-source heterogeneous attributes, learn case embeddings and retrieve top-K similar historical cases to recommend potential evolutionary characteristics. Experiments on 474 projects across China’s four major urban agglomerations yield a semantic overlap score of 0.9277 and a top-3 precision of 0.7817, demonstrating the method’s utility for knowledge-based urban renewal decision support.","随着城市化从外延式扩张转向建成环境再生，可持续城市更新日益依赖对历史案例的有效复用。然而，这一过程仍受制于多源地理参照数据碎片化、语义异质性以及案例评估能力有限等问题。为应对上述挑战，本研究提出GeoCaseRec，一种知识图谱语义驱动的城市更新推荐框架。该框架以地理案例推理为基础，构建了涵盖地理位置、场地条件、空间拓扑与演化特征四个维度的集成表示模型，并进一步引入结合大语言模型与空间计算的联合抽取方法，从异构来源中提取关键信息。基于这些数据，构建案例内空间拓扑与案例间语义层级，实现城市更新知识图谱的自动化构建。为弥合推荐阶段的技术缺口，GeoCaseRec采用异构图神经网络融合多源异构属性，学习案例嵌入并检索前K个相似历史案例，以推荐潜在的演化特征。在中国四大城市群474个项目上的实验取得了0.9277的语义重叠得分和0.7817的前3精度，表明该方法对基于知识的城市更新决策支持具有实用价值。",null,"International Journal of Digital Earth","2026-10-01T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦城市更新推荐，与三农、农业信息化、智慧农业、数字乡村等主题无关，不予入选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":36},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":33,"tags":35},"I2",3,true,[],[34],"decision support",[],"农业相关性需要复核",[],"GeoCaseRec:aknowledgegraphsemantics-driv-4107",1,"10.1080\u002F17538947.2026.2738331",{"doi":40,"openalex_id":42,"authors":43,"venue":10,"cited_by_count":15,"oa_url":64,"card":65,"direction":69,"ingested_from":71},"W7215035152",[44,47,49,52,54,56,59,61],{"name":45,"orcid":46},"Minmin Li","https:\u002F\u002Forcid.org\u002F0000-0003-0189-781X",{"name":48,"orcid":9},"Shilong Wei",{"name":50,"orcid":51},"Shunli Wang","https:\u002F\u002Forcid.org\u002F0000-0001-6663-4960",{"name":53,"orcid":9},"Yafei Wang",{"name":55,"orcid":9},"Jinghao Gu",{"name":57,"orcid":58},"Ding Ma","https:\u002F\u002Forcid.org\u002F0000-0001-9328-9584",{"name":60,"orcid":9},"Ze Liu",{"name":62,"orcid":63},"Renzhong Guo","https:\u002F\u002Forcid.org\u002F0000-0001-9376-2618","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17538947.2026.2738331?needAccess=true",{"tldr":66,"method":67,"finding":68,"direction":69,"opportunity":70},"提出知识图谱语义驱动的城市更新推荐框架GeoCaseRec，自动检索相似历史案例并推荐演化特征。","地理案例推理、大模型与空间计算联合抽取、异构图神经网络，474个中国城市更新项目","语义重叠得分0.9277，top-3精度0.7817，验证了知识化城市更新决策支持的有效性。","数字乡村与农业信息化","可将该知识图谱+案例推理范式迁移至乡村建设或农业设施更新，解决多源异构数据与案例复用难题。","openalex","2026-10-03T23:30:46.929154Z",{"total":15,"page":39,"page_size":39,"items":74},[]]