Graphusion: A RAG Framework for Knowledge Graph Construction with a Global Perspective
Paper • 2410.17600 • Published • 1
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TutorQA Benchmark
This dataset is part of the benchmark introduced in the paper Graphusion: Leveraging Large Language Models for Scientific Knowledge Graph Fusion and Construction in NLP Education. We also release more data in our GitHub page. It contains 6 tasks designed for evaluating various aspects of reasoning, graph understanding, and language generation.
Dataset Structure
Each task is a separate split:
task1: Relation Judgmenttask2: Prerequisite Predictiontask3: Path Searchingtask4: Subgraph Completiontask5: Clusteringtask6: Idea Hamster (no answers, open ended)
| Split | Fields |
|---|---|
| task1 | question, answer |
| task2 | question, answer |
| task3 | question, answer |
| task4 | question, answer |
| task5 | question, answer |
| task6 | question |
Usage Example
from datasets import load_dataset
dataset = load_dataset("li-lab/tutorqa")
# Access individual tasks
task1 = dataset["task1"]
task6 = dataset["task6"]
Citation
@inproceedings{yang2025graphusion,
title={Graphusion: A RAG Framework for Knowledge Graph Construction with a Global Perspective},
author={Yang, Rui and Yang, Boming and Feng, Aosong and Ouyang, Sixun and Blum, Moritz and She, Tianwei and Jiang, Yuang and Lecue, Freddy and Lu, Jinghui and Li, Irene},
booktitle={Proceedings of the NLP4KGC Workshop at The Web Conference 2025 (WWW'25)},
year={2025},
url={https://arxiv.org/abs/2410.17600}
}
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