Directed Acyclic Graphs (DAGs) are widely used to represent structured knowledge in scientific and technical domains. However, datasets for real-world DAGs remain scarce because constructing them typically requires expert interpretation of domain documents. We study Doc2SemDAG construction: recovering a preferred semantic DAG from a document together with the cited evidence and context that explain it. This problem is challenging because a document may admit multiple plausible abstractions, the intended structure is often implicit, and the supporting evidence is scattered across prose, equations, captions, and figures. To address these challenges, we leverage scientific papers containing explicit DAG figures as a natural source of supervision. In this setting, the DAG figure provides the DAG structure, while the accompanying text provides context and explanation. We introduce DAGverse, a framework for constructing document-grounded semantic DAGs from online scientific papers. Its core component, DAGverse-Pipeline, is a semi-automatic system designed to produce high-precision semantic DAG examples through figure classification, graph reconstruction, semantic grounding, and validation. As a case study, we test the framework for causal DAGs and release DAGverse-1, a dataset of 108 expert-validated semantic DAGs with graph-level, node-level, and edge-level evidence. Experiments show that DAGverse-Pipeline outperforms existing Vision-Language Models on DAG classification and annotation. DAGverse provides a foundation for document-grounded DAG benchmarks and opens new directions for studying structured reasoning grounded in real-world evidence.

翻译：有向无环图（DAG）在科技领域被广泛用于表示结构化知识。然而，现实世界的DAG数据集仍然稀缺，因为构建这类数据通常需要专家对领域文档进行解读。本文研究Doc2SemDAG构建：从文档中恢复包含引证证据和解释上下文的预期语义DAG。这一挑战源于文档可能存在多种合理抽象，其预期结构往往隐含不显，且支撑证据分散于正文、公式、图注和图表中。为解决这些问题，我们利用包含显式DAG图形的科学论文作为天然监督源。在此设定下，DAG图形提供图结构，伴随文本提供上下文与解释。我们提出DAGverse框架——一种从在线科学论文构建文档锚定语义DAG的系统框架。其核心组件DAGverse-Pipeline是一个半自动化系统，通过图形分类、图重构、语义锚定和验证生成高精度语义DAG示例。作为案例研究，我们在因果DAG场景测试该框架并发布DAGverse-1数据集，包含108个经专家验证的语义DAG及其图级、节点级和边级证据。实验表明，DAGverse-Pipeline在DAG分类与标注任务上超越现有视觉-语言模型。DAGverse为文档锚定DAG基准测试奠定基础，并开辟了基于现实证据的结构化推理研究新方向。