LearnALCLengths

This repository contains our implementation of concept length predictors in the ALC description logic.

Installation

• Clone this repository:

https://github.com/dice-group/LearnALCLengths.git

• Install Anaconda3, then all required librairies by executing the following commands (Linux):

1. conda create -n clip python==3.11.5 && conda activate clip
2. pip install -r requirements.txt
3. git clone https://github.com/dice-group/Ontolearn.git && cd Ontolearn && git checkout 0.5.4 && pip install -e .

• Download DL-Learner-1.4.0 from github and extract it into this repository (cloned above)

• Clone DLFoil and DLFocl dlfoil, dlfocl, and extract the two repositories into LearnALCLengths/

• Install Java (version 8+) and Apache Maven (Only necessary for running DL-Learner and DL-Foil/DL-Focl)

Reproducing the reported results

Datasets (necessary for running the algorithms)

• Download datasets and extract the zip file into LearnALCLengths/ and rename the folder as Datasets

CLIP (our method)

*Open a terminal and navigate into /reproduce_results/ cd LearnALCLengths/reproduce_results/

• Reproduce CLIP concept learning results on all KBs sh reproduce_celoe_clp_experiment_all_kbs.sh
• Reproduce the training of concept length predictors sh reproduce_training_clp_on_all_kbs.sh
• Furthermore, one can train concept length predictors on a single knowledge base as follows python reproduce_training_length_predictors_K_kb.py, where K is one of carcinogenesis, mutagenesis, semantic_bible or vicodi. Use -h to see more training options (example python reproduce_training_length_predictors_carcinogenesis_kb.py -h ).

CELOE, ELTL, OCEL from DL-Learner

*Open a terminal and navigate into /other_learning_systems/scripts cd LearnALCLengths/dllearner/scripts

• Reproduce concept learning results on knowledge base K for algorithm Algo python reproduce_dllearner_experiment.py --learning_systems Algo --knowledge_bases K
• To reproduce the results for multiple algorithms on multiple knowledge bases, use the schema python reproduce_dllearner_experiment.py --learning_systems Algo1 Algo2... --knowledge_bases K1 K2...

Note that Algo is one of celoe, ocel or eltl, and K is one of carcinogenesis, mutagenesis, semantic_bible or vicodi (all lower cased)

DLFoil and DLFocl

For DLFoil, open a terminal and navigate into /dl-foil/DLFoil2 cd LearnALCLengths/dl-foil/DLFoil2

• Run mvn clean install
• Open a different terminal and run the following python LearnALCLengths/generators/generate_dlfoil_config_all_kbs.py
• Now execute the following in the first terminal (in LearnALCLengths/dl-foil/DLFoil2): mvn -e exec:java -Dexec.mainClass=it.uniba.di.lacam.ml.DLFoilTest -Dexec.args=K_config.xml >> ../dlfoil_out_K.txt, where K is one of carcinogenesis, mutagenesis, semantic_bible or vicodi.

Note that DLFoil fails to solve our learning problems as it gets stuck on the refinement of certain partial descriptions.

We could not run DLFocl.

The authors did not provide sufficient documentation to run their algorithm; the documentation is here

Statistical Test

Open a terminal and navigate into /reproduce_results/ cd LearnALCLengths/reproduce_results/

• Run Wilcoxon statistical test on concept learning results All Algos vs CLIP: sh run_statistical_test_on_all_kbs.sh

Use your own data

• Add your data into Datasets: it should be a folder containing a file formatted as RDF/XML or OWL/XML and should have the same name as the folder.

• Navigate into /generators and run python train_data/generate_training_data.py --kb your_folder_name, use -h to see more options. The generated file Data.json under your_folder_name/Train_data/ should serve for training concept length predictors, see example scripts in /reproduce_results/train_clp/.

• Similarly, learning problems can be generated using one of the example files in generators/learning_problems/ (replace folder names by your folder name)

• Navigate into /Embeddings/Compute-Embeddings/ and run the following to embed your knowledge base: python run_script.py --path_dataset_folder your_folder_name

• Train concept length predictors by preparing and running your python file reproduce_training_length_predictors_K_kb.py following examples in /reproduce_results/train_clp/.

• Finally, prepare a script (see examples in /reproduce_results/celoe_clp/) and run CLIP on your data.

Acknowledgement

We based our implementation on the open source implementation of ontolearn. We would like to thank the Ontolearn team for the readable codebase.