SPELL (SAT-bases PAC EL concept Learner) is an implementation of a sample-efficient learning algorithm for EL-concepts under ELHr-ontologies. It takes as input a knowledge base (formulated in the description logic ELHr) and lists of individuals from that knowledge base that are positive or negative examples. It then learns an EL-concept that fits the examples with respect to the provided background knowledge.
More information on SPELL and the theory behind it is available in the paper SAT-based PAC Learning of Description Logic Concepts
You can find instructions on how to reproduce the benchmarks in benchmarks.md
Contact Maurice Funk mfunk@informatik.uni-leipzig.de if you have any questions or comments
These instructions were tested with python 3.10.9 on macOS.
Create a python virtual environment (to avoid installing dependencies in the global environment):
python -m venv spell-venv
Enter the virtual environment:
source ./spell-venv/bin/activate
Install dependencies:
pip install -r requirements.txt
Make sure that the robot tool is available in the robot directory (this is required for some tests):
cd robot
./get_robot.sh
cd ..
Check that everything works by running the tests:
pytest
Run an example:
python spell_cli.py tests/father.owl tests/father-example/P.txt tests/father-example/N.txt
See
python spell_cli.py --help
for some options.
Run the demo webui:
pip install flask
python -m webui.spell_webui