Summary
Directed acyclic graphs (DAGs) are widely used to represent structured knowledge in science and technology, yet real-world DAG datasets remain scarce. DAGverse (arXiv:2603.25293) is 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 graph classification, graph reconstruction, semantic anchoring, and validation. The paper addresses the Doc2SemDAG task: recovering preferred semantic DAGs from documents together with the citation evidence and context that explain them. As a case study, the framework was tested on causal DAGs, and the authors released DAGverse-1, a dataset of 108 expert-verified semantic DAGs. The work, authored by Shu Wan, Saketh Vishnubhatla, Iskander Kushbay, Tom Heffernan, Aaron Belikoff, and colleagues, targets NLP researchers working on knowledge graph construction and scientific document understanding.
Paper Overview
Field: NLP
Authors: Shu Wan, Saketh Vishnubhatla, Iskander Kushbay, Tom Heffernan, Aaron Belikoff, et al.
arXiv: 2603.25293
Abstract
Directed acyclic graphs (DAGs) are widely used to represent structured knowledge in science and technology. However, datasets of real-world DAGs remain scarce. This paper studies the construction of Doc2SemDAG: recovering preferred semantic DAGs from documents along with the citation evidence and context that explain them.
The authors introduce DAGverse, a framework for building 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:
- Graph classification
- Graph reconstruction
- Semantic anchoring
- Validation
As a case study, the framework was tested on causal DAGs, and the authors released DAGverse-1, a dataset of 108 expert-verified semantic DAGs.
Links
- Paper: https://arxiv.org/abs/2603.25293
---
*Auto-collected on 2026-03-29.*
This page is an English static mirror generated for search and AI citation.
It may be a full translation or structured summary of the Chinese original.
Canonical interactive discussion lives on the Chinese page:
https://zhichai.net/topic/177169403