HELIOS is a hyper-relational schema model, which directly learns from hyper-relational schema tuples in a KG. HELIOS captures not only the correlation between multiple types of a single entity, but also the correlation between types of different entities and relations in a hyper-relational schema tuple. Please see the details in our paper below:

• Yuhuan Lu, Bangchao Deng, Weijian Yu, and Dingqi Yang. 2023. HELIOS: Hyper-Relational Schema Modeling from Knowledge Graphs. In Proceedings of the 31st ACM International Conference on Multimedia (MM ’23), October 29–November 3, 2023, Ottawa, ON, Canada.

python run.py --dataset jf17k --gpu 0

python run.py --dataset wikipeople --gpu 0

python run.py --dataset wd50k --gpu 0

The datasets are available here: https://www.dropbox.com/s/iz5wxp0uldx5i05/data.zip?dl=0 , and put them into the data folder.

In run.py, you can set:

--dataset: input dataset

--epochs: number of training epochs

--batch_size: batch size of training set

--dim: embedding size

--learning_rate: learning rate

--self_attention_layers: number of self-attention layers

--gat_layers: number of GAT layers

--gpu: gpu to be used for train and test the model

--num_attention_heads: number of attention heads

Python: 3.7.13

torch: 1.11.0

If you use our code or datasets, please cite:

@inproceedings{lu2023helios,
  title={HELIOS: Hyper-Relational Schema Modeling from Knowledge Graphs},
  author={Lu, Yuhuan and Deng, Bangchao and Yu, Weijian and Yang, Dingqi},
  booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
  pages={4053--4064},
  year={2023}
}