dket: Deep Knowledge Extraction from Text

The DKET project has the goal to devise a Neural Networks based Ontology Learning system that doesn't rely on hand-crafted rules and it is trained in an end-to-end fashion. The approach, the datasets, and the experimental evaluation are described in this paper.

Installation

The best way to install and use the DKET package is to clone the git repository, and set up the proper virtual environment. Once cloned the repository, just move into the directory and run the proper script to create and setup the proper Python 3 virtual environment.

:~$ git clone git@github.com:dkmfbk/dket.git
:~$ cd dket
:~$ ./bin/dket-venv-setup gpu

or just run ./bin/dket-venv-setup if you don't have a GPU card on your machine. This will create a .py3venv directory and install all the dependencies you need. Otherwise, You can install DKET as a regular Pyhton package via pip. Since DKET uses the LiTeFlow library, you must resolve the link dependency during the installation via the --process-dependency-links directive:

:~$ pip install --process-dependency-links https://github.com/dkmfbk/dket.git

But after installed DKET you need to install the proper TensorFlow version, for GPU or CPU, by yourself.

Run the experiments

All the experimental settings are stored as .json files in the experiments folder. To run them, after activating the .py3venv virtual environment, just run:

:~$ ./bin/dket-experiment-run --config experiments/<EXP>.json

where <EXP> is the name of the experimental setting that you want to use. The full options for the ./bin/dket-experiment-run are available at:

:~$ ./bin/dket-experiment-run --help

Resources

The folder resources contains:

• the manually curated dataset: 500 sentence-formula pairs, 75 used for training (reference_set.train.tsv) and 425 used for testing (reference_set.test.tsv);
• the grammar (grammar.cfg) used for generating the synthetic datasets.