Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs

Related tags

Deep Learning PERM-GaussianKG
Overview

Implementation for the paper:

Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs, Nurendra Choudhary, Nikhil Rao, Sumeet Katariya, Karthik Subbian and Chandan Reddy, NeurIPS 2021.

Requirements

torch==1.2.0
tensorboardX==1.6

Run

To reproduce the results on FB15k-237, DRKG and NELL, the hyperparameters are set in example.sh.

bash run.sh

Arguments:

--do_train : Boolean that indicates if model should be trained
--cuda : Boolean that indicates if cuda should be used
--do_valid : Boolean that indicates if model should use validation
--do_test : Boolean that indicates if model should be tested to log metrics
--data_path : Folder that contains train, test and validation 
--model : Use 2-dimensions or one dimension for the model
-n : Number of negative samples per positive sample
-b : Batch size for training
-d : Dimension of embeddings (should be equal to semantic vector dimensions)
-lr : Learning rate of the model
--max_steps : Max number of epochs
--cpu_num : number of CPUs
--test_batch_size : Batch size for testing
--center_reg : Regularization factor for center updates
--geo : Gaussian embeddings or Vec embeddings
--task : Tasks for training
--stepsforpath : Same as number of epochs
--offset_deepsets : Aggregation methods for offsets
--center_deepsets : Aggregation methods for centers
--print_on_screen : Output should print on screen

Code details

dataloader.py - File to load data for the PERM models
model_gaussian.py - File with the model definition for the PERM model and baselines
main_gaussian.py - File to run the model for different experiments
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Comments
  • Error: tensor size does match and some other question

    Error: tensor size does match and some other question

    Hi, your work is very interesting and inspiring. However, I had some difficulties when I tried to repeat your experiments.

    1. When I try your first code in run.sh (I just changed the dimension to 100 and keep all other arguments), it raises the error of tensor size mismatching. In fact, it occurs when i in some tasks. (I gauss there are some problems in the CenterIntersection class.)
    2. In your paper, the part of the intersection seems to be non-parameterized, I wonder what the role of the two networks (GaussianOffsetIntersection and CenterIntersection ) on the intersection is in your model.
    3. I gauss self.rank is the 'r' mentioned in Implementation Details in your paper. But why is the dimension of the center also multiplied by the self.rank? (For example, when I set the dimension to 100, the relation_embedding and offset_embedding are both 1600d. 16 is the self.rank.)
    4. It seems that your memory L (size: d * r) instead of S=L*L^T as the inverse of the covariance matrix. However, I find you implement S_1+S_2 by L_1+L_2, but L_1L_1^T + L_2L_2^T is not the same as (L_1+L_2)(L_1+L_2)^T. (line 242-249 in models_gaussian.py)

    I am sorry for asking you so many questions. Your work is so wonderful that I really look forward to hearing from you so that I can fully understand your work.

    opened by gary-young 1
Owner
Nurendra Choudhary
PhD Student
Nurendra Choudhary
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