Neural Methods for Logical Reasoning over Knowledge Graphs
Authors: Alfonso Amayuelas, Shuai Zhang, Xi Susie Rao, Ce Zhang
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate experimentally the performance of our model through extensive experimentation on well-known benchmarking datasets. |
| Researcher Affiliation | Academia | EPFL ETH Zurich alfonso.amayuelas@alumni.epfl.ch {shuazhang, raox, ce.zhang}@inf.ethz.ch |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code available on: https://github.com/amayuelas/NNKGReasoning |
| Open Datasets | Yes | We perform experiments on three standard datasets in KG benchmarks. These are the same datasets used in Query2Box (Ren et al., 2020) and Beta E (Ren & Leskovec, 2020): FB15k (Bordes et al., 2013), FB15k-237 (Toutanova et al., 2015) and NELL995 (Xiong et al., 2017b). |
| Dataset Splits | Yes | In the experiments, we use the standard evaluation scheme for Knowledge Graphs, where edges are split into training, test and validation sets. ... we effectively create 3 graphs: G train for training; G valid, which contains G train plus the validation edges; and G test which contains G valid and the test edges. |
| Hardware Specification | Yes | All experiments have been computed on independent processes on NVIDIA GPUs, either the Ge Force GTX Titan X Pascal (12 GB) or the Tesla T4 (16 GB). |
| Software Dependencies | No | The paper states 'Our code is implemented using Py Torch.' but does not provide specific version numbers for PyTorch or any other software dependencies, such as Python or CUDA. |
| Experiment Setup | Yes | All our models and GQE use the following parameters: Embed dim = 800, learning rate = 0.0001, negative sample size = 128, batch size = 512, margin = 24, num. iterations = 300,000/450,000. Q2B and Beta E differ from the previous configuration in Embed. dim = 400 and margin = 30/60. |