Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion
Authors: Zhanqiu Zhang, Jianyu Cai, Jie Wang
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we introduce the experimental settings in Section 5.1 and show the effectiveness of DURA in Section 5.2. We compare DURA to other regularizers in Section 5.3 and visualize the entity embeddings in Section 5.4. Finally, we analyze the sparsity induced by DURA in Section 5.5. |
| Researcher Affiliation | Academia | University of Science and Technology of China {zzq96,jycai}@mail.ustc.edu.cn,jiewangx@ustc.edu.cn |
| Pseudocode | No | The paper describes mathematical formulations and derivations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of HAKE is available on Git Hub at https://github.com/MIRALab-USTC/KGE-DURA. |
| Open Datasets | Yes | We consider three public knowledge graph datasets WN18RR [27], FB15k-237 [6], and YAGO3-10 [17] for the knowledge graph completion task, which have been divided into training, validation, and testing set in previous works. |
| Dataset Splits | Yes | We consider three public knowledge graph datasets WN18RR [27], FB15k-237 [6], and YAGO3-10 [17] for the knowledge graph completion task, which have been divided into training, validation, and testing set in previous works. The statistics of these datasets are shown in Table 1. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not explicitly state specific software dependencies or their version numbers. |
| Experiment Setup | Yes | We search λ in {0.005, 0.01, 0.05, 0.1, 0.5} and λ1, λ2 in {0.5, 1.0, 1.5, 2.0}. We reimplement CP, Dist Mult, Compl Ex, and RESCAL using the reciprocal setting [15, 14]. |