/traversing_knowledge_graphs

Primary LanguagePythonMIT LicenseMIT

This repository contains the code, data, and parameters used in the following paper.

Kelvin Guu, John Miller, Percy Liang. Traversing Knowledge Graphs in Vector Space Empirical Methods in Natural Language Processing (EMNLP), 2015.

If you use the dataset/code in your research, please cite the above paper.

@inproceedings{gu2015traversing,
	author = {K. Guu and J. Miller and P. Liang},
	booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
	title = {Traversing Knowledge Graphs in Vector Space},
	year = {2015},
}

Reproducibility: A Codalab worksheet containing all of our experiments and several executable examples is available here.

Data

To automatically download the datasets used in our experiments, call the script get_datasets.sh.

data/

  • freebase/
    • train
    • dev
    • test
  • wordnet/
    • train
    • dev
    • test

data format:

  • Each line represents one (source, relation, target) triple. Elements of the triple are separated by tabs.
  • In addition to test, we also include test_induction and test_deduction. These correspond to the splits of the same name described in the paper.

To automatically download our parameters, call the script get_parameters.sh.

params/

  • freebase/
    • transE
    • bilinear_diag
    • bilinear
  • wordnet/
    • train
    • dev
    • test

params format:

  • Each file contains a pickled SparseVector object (pickled with cPickle).
  • COMP files contain parameters that have been trained on the compositional dataset. SINGLE files contain parameters that have only been trained on single edge queries.

Code

To run an experiment using our code, call

python demo.py CONFIG DATAPATH

from the code directory. CONFIG details the hyperparameters for the model and is defined in config.py. DATAPATH specifies a path to one of the datasets in data or your own dataset.