摘要：学术知识图谱（Scholarly Knowledge Graphs，SKG）为文献检索、科学发现以及 Agent 辅助科研提供了关键支持。然而，目前大多数 SKG 构建方法往往会预先指定要表示的知识单元——通常被称为学术实体或贡献级陈述（contribution-level statements），并且仅保留能够通过该单元表达的文献内容。这种策略催生了两种占主导地位却相互割裂的范式：以概念为中心、保留实体及局部关系的 SKG，以及以陈述为中心、捕获句子中高阶话语的 SKG。在这两种范式下，图谱反映的均是人为预设的抽取目标，而非论文本身构建和传达知识的真实脉络。我们推出了学术高阶话语本体 SUDO（Scholarly Upper Discourse Ontology），它明确定义了知识要素应如何组织并相互关联以构成论文的研究叙事，即其学术话语（scholarly discourse）。基于可跨领域和跨任务泛化的语言学原则，SUDO 使得每篇文献的学术话语能够自底向上构建为连贯且有据可依的原生表示。通过在多篇论文间统一应用，该结构可将各自的研究叙事无缝连接为全球学术话语网络，且不会丢失局部上下文或来源追溯信息。我们在涵盖学术知识建模的全面用例中验证了 SUDO 的各项能力，并证明了其在下游基于大语言模型（LLM）的科学问答（QA）任务中所带来的性能提升。

Abstract:Scholarly Knowledge Graphs (SKGs) support literature search, scientific discovery, and agent-assisted research. Yet, most approaches to SKG construction pre-specify the knowledge unit to be represented, typically named scholarly entities or contribution-level statements, and retain only the publication content expressible through that unit. This choice produces two dominant yet distinct paradigms: concept-centric SKGs that preserve entities and local relations, and statement-centric SKGs that capture high-level discourse in sentences. In both cases, the graph reflects a chosen extraction target rather than how a paper constructs and communicates knowledge. We introduce SUDO, the Scholarly Upper Discourse Ontology, which defines how knowledge elements are organized and related to form a paper's research narrative, namely its scholarly discourse. Using linguistic principles that generalize across fields and tasks, SUDO enables each publication's scholarly discourse to be constructed from the bottom up as a coherent, source-grounded representation. Applied consistently across papers, the same structure connects their research narratives into a global scholarly discourse without discarding local context or provenance. We demonstrate SUDO's capabilities across a comprehensive suite of scholarly knowledge-modeling use cases and performance improvements on a downstream Large Language Model (LLM)-based scientific QA task.