Hengzongshu/ArticleAgent

NSU Academic Concept Paths Dataset

This dataset supports the research presented in:
"Constraint-Driven Small Language Models Based on Agent and OpenAlex Knowledge Graph: Mining Conceptual Pathways and Discovering Innovation Points in Academic Papers"
by Ziye Xia and Sergei S. Ospichev (2025).

It contains curated academic data from Novosibirsk State University (NSU), annotated with structured concept paths and innovation points grounded in the OpenAlex knowledge graph.

📁 Files

• train.json: Training set (structured instruction-tuning data for the 4-stage agent pipeline)
• val.json: Validation set
• test.json: Test set (includes human-annotated innovation points)
• concept_paths.json: Full list of 84,181 extracted concept paths
• innovation_annotations.json: 1,196 expert-validated innovation points

📊 Statistics

Item	Count
Papers	7,960
Unique OpenAlex Concepts	11,446
Concept Paths	84,181
Innovation Annotations	1,196
Concept Associations (validated)	127,203

🧠 Data Format (Example from train.json)

Each sample follows an instruction-tuning format:

{
  "instruction": "Extract concept pairs from the research methods section.",
  "input": "<research_methods>... text ...</research_methods>",
  "output": "[[\"Physics\", \"Superconductivity\"], [\"Materials Science\", \"High-Tc materials\"]]"
}