TFBA

Codes for Tensor Factorization with Back-off and Aggregation.

Prerequisites:

install sktensor (https://github.com/mnick/scikit-tensor)

This package contains the following files:

• dataGen.py -- Used to generate tensors from the set of tuples.
• factorize.py -- Joint tensor factorization.
• cliqueMine.py -- Constrained Clique mining.

Running Instructions:

• python2.7 dataGen.py <tuples_file> <output_dir>
  --- Each line in the input file is a tab separated 4-tuple of the format subject "\t" relation "\t" object "\t" other "\t" frequency.
  --- 3-tuples can also be provided in the same file along with 4-tuples, in which case use the string "" for other.
  --- This script will create pkl files in the output directory.
  

• python2.7 factorize.py <data_dir> <output_dir> [other options]
  --- Performs the factorization and store the latent factor matrices and core tensors in the <output_dir> directory.
  --- <data_dir> should be same as the <output_dir> of dataGen.py.
  optional arguments:
  -h, --help show this help message and exit
  --minLambda MINLAMBDA [MINLAMBDA ...]
  ** Enter the min lambda (list), default = 0.1 0.1 0.1
  --maxLambda MAXLAMBDA [MAXLAMBDA ...]
  ** Enter the max lambda (list), needed only for grid search. If no grid search, provide only minLambda option. --step STEP Enter the step size for grid search (default = 0.5)
  --maxIters MAXITERS Enter the maximum iterations (default = 10)
  --rank1 RANK1 Enter rank1 (default = 10)
  --rank2 RANK2 Enter rank2 (default = 10)
  --rank3 RANK3 Enter rank3 (default = 10)
  --fit FIT Y/N, default = N. Give Y for fit computation.
  --cores CORES Number of Threads
  

• python2.7 cliqueMine.py <data_dir> <output_dir> --rank r1 r2 r3
  --- Performs constrained clique mining and stores the schemas in <output_dir>
  --- <data_dir> should be same as <data_dir> used to run Factorize.py

References:

[1] Madhav Nimishakavi, Manish Gupta and Partha Talukdar. Relation Schema Induction using Tensor Factorization with Back-off and Aggregation. Proceedings of 2018 Conference on Association for Computaional Linguistics (ACL 2018).