On the Representation and Embedding of Knowledge Bases beyond Binary Relations
Authors: Jianfeng Wen, Jianxin Li, Yongyi Mao, Shini Chen, Richong Zhang
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate experimentally that m-Trans H outperforms Trans H by a large margin, thereby establishing a new state of the art. 4 Experiments |
| Researcher Affiliation | Academia | 1 State Key Laboratory of Software Development Environment, Beihang University 2 School of Computer Science and Engineering, Beihang University 3 School of Electrical Engineering and Computer Science, University of Ottawa |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper states: 'To inspire further research on the embedding of multi-fold relations, we have made our JF17K datasets publicly available.2 http://www.site.uottawa.ca/ yymao/JF17K'. This link provides access to datasets, not the source code for the methodology described in the paper. |
| Open Datasets | Yes | To inspire further research on the embedding of multi-fold relations, we have made our JF17K datasets publicly available.2 http://www.site.uottawa.ca/ yymao/JF17K. Dataset Gid was randomly split into training set GXid and testing set G?id where every fact ID entity in G?id was assured to appear in GXid. |
| Dataset Splits | No | The paper mentions 'training set GXid and testing set G?id' for its dataset splits but does not explicitly state the use of a separate 'validation set' or provide details on a validation split. |
| Hardware Specification | Yes | For example, at DIM=50, the training/testing times (in minutes) for Trans H:triple and m-Trans H:ID are respectively 105/229 and 52/135, on a 32-core Intel E5-2650 2.0GHz processor. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their versions). It only mentions 'Stochastic Gradient Descent' as a training method. |
| Experiment Setup | Yes | Several choices of the dimension (DIM) of U are studied. In Trans H:triple and Trans H:inst, for each triple in GXsc, one random negative example is generated. In m-Trans H and m-Trans H:ID, for each instance in GX, random negative examples are generated. This way, the total number of negative examples used in every experiment is the same, assuring a fair comparison. Stochastic Gradient Descent is used for training, as is standard. |