AI 中文总结
研究针对城市数字孪生多源数据整合难题，提出多源溯源感知的3D城市知识图谱AuthentiCity，整合多城市多类型数据。引入两个基准测试系列，通过自然语言到查询翻译及图表示学习评估，展示其在多任务中的优势，为相关研究提供数据及测试支持。
AI 中文摘要
城市数字孪生越来越多地将具有不同可靠性、覆盖范围和语义的权威数据、众包数据、机器学习数据和重建数据结合起来。然而，很少有城市数据集能提供支持多源集成、溯源跟踪、空间推理和机器学习的统一表示。我们提出了真实性（AuthentiCity），这是一个跨三大洲五个城市（汉堡、赫尔辛基、苏黎世、纽约和东京）的多源、溯源感知3D城市知识图谱，包含180 GiB数据、1.8亿个节点、2.2亿条边、12亿个属性和360万个建筑物。其标记属性图整合了所有城市的权威CityGML和OpenStreetMap数据，为汉堡添加了屋顶材料预测和重建的LoD3几何图形，在溯源模型中派生信息不会取代权威数据。置信加权边解决跨源对应关系，构建规范的城市实体，同时保留与贡献证据的可追溯链接。真实性主要是一个数据贡献。我们引入了两个基准测试系列来展示该表示所支持的任务。第一个评估超越传统文本到SQL和文本到Cypher基准测试的自然语言到查询翻译，包括3D空间推理、溯源感知过滤、跨源一致性和不一致性、覆盖感知聚合以及不可行查询检测。第二个通过多源属性预测、节点分类和跨源匹配预测评估图表示学习，实现对无溯源和有溯源嵌入的比较。即使是强大的商业语言模型执行准确率也仅达到54 - 69%，7B开放权重模型为6 - 19%，而开放权重模型对无法回答的问题从不弃权。
英文摘要
Urban digital twins increasingly combine authoritative, crowd-sourced, machine-learned, and reconstructed data with differing reliability, coverage, and semantics. Yet few urban datasets provide a unified representation supporting multi-source integration, provenance tracking, spatial reasoning, and machine learning. We present AuthentiCity, a multi-source, provenance-aware 3D city knowledge graph spanning five cities across three continents (Hamburg, Helsinki, Zurich, New York, and Tokyo) and comprising 180 GiB, 180M nodes, 220M edges, 1.2B properties, and 3.6M buildings. The labeled property graphs integrate authoritative CityGML and OpenStreetMap data for all cities, adding roof-material predictions and reconstructed LoD3 geometry for Hamburg, under a provenance model in which derived information never replaces authoritative data. Confidence-weighted edges resolve cross-source correspondences, constructing canonical urban entities while preserving traceable links to contributing evidence. AuthentiCity is primarily a data contribution. We introduce two benchmark families that demonstrate the tasks enabled by the representation. The first evaluates natural-language-to-query translation beyond conventional text-to-SQL and text-to-Cypher benchmarks, including 3D spatial reasoning, provenance-aware filtering, cross-source agreement and disagreement, coverage-aware aggregation, and infeasible-query detection. The second evaluates graph representation learning through multi-source attribute prediction, node classification, and cross-source matching prediction, enabling comparison of provenance-agnostic and provenance-aware embeddings. Even a strong commercial LLM reaches only 54-69 % execution accuracy and a 7B open-weight model 6-19 %, while the open-weight model never abstains on unanswerable questions.
