Knowledge graphs over corpora of inter-referencing documents - scholarly papers, legal opinions, policy briefs - encode the topology of reference but not its stance. The standard representation collapses a rich evaluative relation into an untyped edge, losing the very content that supports community-level queries about how one document is received by another. We propose the claim network: a representational pattern in which each cross-document reference is reified as a typed claim, carrying source, target, claim text, and a four-class stance label grounded in the citation-intent literature. We give a construction pipeline applicable to any corpus of scholarly inter-referencing documents and instantiate it on a corpus of 127 papers in 3D point cloud semantic segmentation, producing a network of 8,260 typed claims. Three downstream task families demonstrate what the network enables: retrieval signal augmentation, aggregated-stance summarisation, and topological analytics. Head-to-head evaluation against standard Retrieval-Augmented Generation (RAG) baselines shows that the gain over flat retrieval is the gain from the right intermediate representation rather than the wrong one.

翻译：基于相互引用文献语料库（学术论文、法律意见书、政策简报）构建的知识图谱编码了引用的拓扑结构，但并未编码其立场。标准表示将丰富的评价关系坍缩为无类型边，丢失了支持社群级别查询（即一篇文献如何被另一篇文献接收）的核心内容。我们提出主张网络：一种表示模式，其中每个跨文献引用被具体化为类型化主张，包含来源、目标、主张文本以及基于引文意图文献的四类立场标签。我们给出了一个适用于任何学术互引文献语料库的构建流水线，并在包含127篇三维点云语义分割论文的语料库上实例化，生成了一个包含8,260个类型化主张的网络。三个下游任务家族展示了该网络所支持的功能：检索信号增强、聚合立场摘要和拓扑分析。与标准检索增强生成基线进行正面评估表明，相较于平面检索的提升正来自正确而非错误的中间表示。