DAGverse: Building Document-Grounded Semantic DAGs from Scientific Papers
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 ...
| Authors | Shu Wan, Saketh Vishnubhatla, Iskander Kushbay et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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