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Aryan95614/aerograph-asrs

AeroGraph ASRS Dataset 2,000 real NASA Aviation Safety Reporting System (ASRS) incident reports with LLM-extracted entities and relations for knowledge graph construction. Dataset Description This dataset contains processed ASRS incident narratives along with structured entity and relation extractions conforming to an aviation safety ontology (10 entity types, 8 edge types). Reports Split 2000 reports from the NASA ASRS database Fields: id, text… See the full description on the dataset page: https://huggingface.co/datasets/Aryan95614/aerograph-asrs.

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Dataset Card

AeroGraph ASRS Dataset

2,000 real NASA Aviation Safety Reporting System (ASRS) incident reports with LLM-extracted entities and relations for knowledge graph construction.

Dataset Description

This dataset contains processed ASRS incident narratives along with structured entity and relation extractions conforming to an aviation safety ontology (10 entity types, 8 edge types).

Reports Split

  • —2000 reports from the NASA ASRS database
  • —Fields: id, text, aircrafttype, phaseofflight, anomalytype
  • —Average narrative length: ~250 words

Extractions Split

  • —3035 extraction results (one per report)
  • —65,025 total entities extracted
  • —59,263 total relations extracted
  • —Entity types: Aircraft, Event, Phase, Factor, Component, Outcome, Recommendation, ATC_Facility, Weather, TimePeriod
  • —Edge types: CAUSEDBY, CONTRIBUTEDTO, OCCURREDDURING, INVOLVED, RESOLVEDBY, PRECEDEDBY, COOCCURREDWITH, TEMPORALSEQUENCE

Usage

python
from datasets import load_dataset

# Load reports
reports = load_dataset("Aryan95614/aerograph-asrs", "reports", split="reports")

# Load extractions
extractions = load_dataset("Aryan95614/aerograph-asrs", "extractions", split="extractions")

Citation

bibtex
@misc{dhawan2026aerograph,
  title={AeroGraph: Graph-Augmented Retrieval for Multi-Hop Causal
         Reasoning over Aviation Safety Reports},
  author={Dhawan, Aryan},
  year={2026},
  url={https://github.com/Aryan95614/AeroGraph}
}

License

MIT