Embedding of Hierarchically Typed Knowledge Bases
Authors: Richong Zhang, Fanshuang Kong, Chenyue Wang, Yongyi Mao
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments We conduct experiments to evaluate the proposed typed models (Trans E-T and Trans H-T on binary data and m Trans H-T on multi-fold data) and their corresponding typeless models. |
| Researcher Affiliation | Academia | Richong Zhang,1 Fanshuang Kong,1 Chenyue Wang,1 Yongyi Mao2 1BDBC and SKLSDE, School of Computer Science and Engineering, Beihang University 2School of Electrical Engineering and Computer Science, University of Ottawa |
| Pseudocode | No | The paper describes the proposed scheme and models using mathematical equations and textual explanations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | More implementation details of the model can be found in the code on GITHUB2. 2https://github.com/kongfansh/Embedding of Hierarchically Typed KB |
| Open Datasets | Yes | Three datasets FB15K, FB15K*, and JF17K are used in the experiments. Both FB15K (Bordes et al. 2013) and JF17K (Wen et al. 2016) contain filtered data obtained from Freebase. |
| Dataset Splits | No | Table 1 lists "#instances(total/train/test)" for each dataset, indicating the data is split into training and testing sets, but a separate validation set split is not explicitly mentioned with quantities or percentages within the paper. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as CPU or GPU models, memory, or cloud infrastructure specifications. |
| Software Dependencies | No | The paper mentions "SGD" (stochastic gradient descent) and refers to code on "GITHUB" but does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | In all the models, SGD initializes all entity embedding vectors and all br vectors to random unit-length vectors. For all typed models, each ac vector is also initialized to random unit-length vectors, and each dc is initialized to 1. For m Trans H, each ar(ρ) is randomly initialized to a value in the interval (0, 1). In SGD, each mini-batch consists of 1000 packets. Each epoch loops over M/1000 batches, where M is the number of instances in the training set. For each model, SGD runs for 1000 epochs. The learning rate in the updates based on the base-cost gradients is set to 0.001, and the learning rate in the updates based on the type-cost gradient is set to 0.0003. |