ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs
Authors: Zhanqiu Zhang, Jie Wang, Jiajun Chen, Shuiwang Ji, Feng Wu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that Con E significantly outperforms existing state-of-the-art methods on benchmark datasets. |
| Researcher Affiliation | Academia | Zhanqiu Zhang1,2 Jie Wang1,2 Jiajun Chen1,2 Shuiwang Ji3 Feng Wu1,2 1CAS Key Laboratory of Technology in GIPAS University of Science and Technology of China 2Institute of Artificial Intelligence Hefei Comprehensive National Science Center {zqzhang,jjchen}@mail.ustc.edu.cn,{jiewangx,fengwu}@ustc.edu.cn 3Texas A&M University sji@tamu.edu |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of Con E is available on Git Hub at https://github.com/MIRALab-USTC/QE-Con E. |
| Open Datasets | Yes | We use three datasets: FB15k [2], FB15k-237 (FB237) [27], and NELL995 (NELL) [30]. |
| Dataset Splits | Yes | We first build three KGs: the training KG Gtrain, the validation KG Gvalid, and the test KG Gtest using training edges, training+validation edges, training+validation+test edges, respectively. Given a test (validation) query q, we aim to discover non-trivial answers Jq Ktest\Jq Kvalid (Jq Kvalid\Jq Ktrain). |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Adam [13] as the optimizer', but it does not provide specific version numbers for any software libraries, frameworks, or programming languages used (e.g., PyTorch version, Python version). |
| Experiment Setup | Yes | We use Adam [13] as the optimizer, and use grid search to find the best hyperparameters based on the performance on the validation datasets. For the search range and best hyperparameters, please refer to Appendix B.2. |