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.
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 [email protected]: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.
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
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.