Text-Enhanced Representation Learning for Knowledge Graph
Authors: Zhigang Wang, Juanzi Li
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on multiple benchmark datasets show that our proposed method successfully addresses the above issues and significantly outperforms the state-of-the-art methods. |
| Researcher Affiliation | Academia | Zhigang Wang and Juanzi Li Tsinghua University, Beijing, CHINA wangzg14@mails.tsinghua.edu.cn lijuanzi@tsinghua.edu.cn |
| Pseudocode | No | The paper describes the overall framework and steps involved in the proposed method, but it does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | For the knowledge graphs to be represented, we employ several datasets commonly used in previous methods, which are generated from Word Net [Miller, 1995] and Freebase [Bollacker et al., 2008]. Following [Bordes et al., 2013; Wang et al., 2014b; Lin et al., 2015b; Socher et al., 2013], we adopt four benchmark datasets for evaluation, which are WN18 and WN11 generated from Word Net, FB15K and FB13 generated from Freebase. |
| Dataset Splits | Yes | The detailed statistics of the datasets are shown in Table 1. Dataset #R #E #Triples(Train/Valid/Test) WN18 18 40,943 141,442 5,000 5,000 FB15K 1,345 14,951 483,142 50,000 59,071 WN11 11 38,696 112,581 2,609 10,544 FB13 13 75,043 316,232 5,908 23,733 |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions training a 'word2vec model' and using 'stochastic gradient descent (SGD)', but it does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We set the neighboring threshold on the co-occurrence network to be 10, and select learning rate λ for SGD among {0.1, 0.01, 0.001}, the margin γ among {1, 2, 4}, the embedding dimension k among {20, 50, 100}, the batch size B among {120, 1440, 4800}. The best configuration is determined according to the mean rank in validation set. We traverse all the training triples for 1,000 times. |