Quantum Embedding of Knowledge for Reasoning
Authors: Dinesh Garg, Shajith Ikbal, Santosh K. Srivastava, Harit Vishwakarma, Hima Karanam, L Venkata Subramaniam
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated the performance of E2R on two different kinds of tasks (i) link prediction task, and (ii) reasoning task. For link prediction, we chose FB15K and WN18 datasets because they are standard in the literature [5, 6, 24]... The experimental results illustrate the effectiveness of E2R relative to standard baselines. |
| Researcher Affiliation | Collaboration | Dinesh Garg1 , Shajith Ikbal1 , Santosh K. Srivastava1, Harit Vishwakarma2 , Hima Karanam1, L Venkata Subramaniam1 1IBM Research AI, India 2Dept. of Computer Sciences, University of Wisconsin-Madison, USA |
| Pseudocode | No | The paper describes its model and loss functions mathematically, but it does not include a dedicated pseudocode or algorithm block. |
| Open Source Code | No | The paper mentions 'We used Open KE (https://github.com/thunlp/Open KE) implementation of these approaches for our evaluation' for baselines, but does not provide any statement or link for the open-sourcing of their own proposed E2R code. |
| Open Datasets | Yes | For link prediction, we chose FB15K and WN18 datasets because they are standard in the literature [5, 6, 24]... To evaluate the reasoning capabilities, we chose LUBM (Lehigh University Benchmark) dataset (http://swat.cse.lehigh.edu/projects/lubm/) |
| Dataset Splits | No | The paper states 'The train and test sets of these datasets are respectively used for training and testing our proposed model.' and 'Tuning of the hyper-parameters for the baseline approaches was performed on the test set for FB15K and WN18 datasets but on the training set for LUBM1U. For E2R, the tuning was always done on the training set.' However, it does not explicitly define a separate validation split or its size/proportion. |
| Hardware Specification | Yes | Our experiments were performed on a Tesla K80 GPU machine. |
| Software Dependencies | No | The paper states 'We implemented E2R model using Py Torch. We used SGD (Stochastic Gradient Descent) with ADAM optimizer [25]', but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | In all our experiments we used d = 100 for E2R model... We used 3 different negative entities per positive entity in our experimental setup. |