Knowledge Hypergraphs: Prediction Beyond Binary Relations
Authors: Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also develop public datasets, benchmarks and baselines for hypergraph prediction and show experimentally that the proposed models are more effective than the baselines. |
| Researcher Affiliation | Collaboration | Bahare Fatemi1,2 , Perouz Taslakian2 , David Vazquez2 and David Poole1 1University of British Columbia 2Element AI |
| Pseudocode | No | Insufficient information. The paper describes the models and their functions but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data available at https://github.com/Element AI/Hyp E. |
| Open Datasets | Yes | The experiments on knowledge hypergraph completion are conducted on three datasets. The first is JF17K proposed by Wen et al. (2016)... We also create two datasets FB-AUTO and M-FB15K from FREEBASE. For the experiments on datasets with binary relations, we use two standard benchmarks for knowledge graph completion: WN18 [Bordes et al., 2014] and FB15k [Bordes et al., 2013]. |
| Dataset Splits | Yes | Table 2: Dataset Statistics. |
| Hardware Specification | No | Insufficient information. The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | Insufficient information. The paper mentions 'Py Torch' but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | Instead of tuning the parameters of Hyp E to get potentially better results, we follow the Kazemi and Poole (2018) setup with the same grid search approach by setting n = 2, l = 2, and s = 2. |