Ontolearn: Learning OWL Class Expression

Ontolearn is an open-source software library for learning owl class expressions at large scale.

Given positive and negative OWL named individual examples $E^+$ and $E^-$, learning OWL Class expression problem refers to the following supervised Machine Learning problem

$$\forall p \in E^+\ \mathcal{K} \models H(p) \wedge \forall n \in E^-\ \mathcal{K} \not \models H(n).$$

To tackle this supervised learnign problem, ontolearn offers many symbolic, neuro-sybmoloc and deep learning based Learning algorithms:

• Drill → Neuro-Symbolic Class Expression Learning
• EvoLearner → EvoLearner: Learning Description Logics with Evolutionary Algorithms
• NCES2 → (soon) Neural Class Expression Synthesis in ALCHIQ(D)
• NCES → Neural Class Expression Synthesis
• NERO → (soon) Learning Permutation-Invariant Embeddings for Description Logic Concepts
• CLIP → Learning Concept Lengths Accelerates Concept Learning in ALC
• CELOE → Class Expression Learning for Ontology Engineering
• OCEL → A limited version of CELOE

Find more in the Documentation.

Installation

pip install ontolearn 

or

git clone https://github.com/dice-group/Ontolearn.git 
# To create a virtual python env with conda 
conda create -n venv python=3.10.14 --no-default-packages && conda activate venv && pip install -e .
# To download knowledge graphs
wget https://files.dice-research.org/projects/Ontolearn/KGs.zip -O ./KGs.zip && unzip KGs.zip

pytest -p no:warnings -x # Running 171 tests takes ~ 6 mins

Learning OWL Class Expression

from ontolearn.learners import TDL
from ontolearn.triple_store import TripleStore
from ontolearn.learning_problem import PosNegLPStandard
from owlapy.owl_individual import OWLNamedIndividual
from owlapy import owl_expression_to_sparql, owl_expression_to_dl
# (1) Initialize Triplestore
# sudo docker run -p 3030:3030 -e ADMIN_PASSWORD=pw123 stain/jena-fuseki
# Login http://localhost:3030/#/ with admin and pw123
# Create a new dataset called family and upload KGs/Family/family.owl
kb = TripleStore(url="http://localhost:3030/family")
# (2) Initialize a learner.
model = TDL(knowledge_base=kb)
# (3) Define a description logic concept learning problem.
lp = PosNegLPStandard(pos={OWLNamedIndividual("http://example.com/father#stefan")},
                      neg={OWLNamedIndividual("http://example.com/father#heinz"),
                           OWLNamedIndividual("http://example.com/father#anna"),
                           OWLNamedIndividual("http://example.com/father#michelle")})
# (4) Learn description logic concepts best fitting (3).
h = model.fit(learning_problem=lp).best_hypotheses()
print(h)
print(owl_expression_to_dl(h))
print(owl_expression_to_sparql(expression=h))

Learning OWL Class Expression over DBpedia

from ontolearn.utils.static_funcs import save_owl_class_expressions

# (1) Initialize Triplestore
kb = TripleStore(url="http://dice-dbpedia.cs.upb.de:9080/sparql")
# (3) Initialize a learner.
model = TDL(knowledge_base=kb)
# (4) Define a description logic concept learning problem.
lp = PosNegLPStandard(pos={OWLNamedIndividual("http://dbpedia.org/resource/Angela_Merkel")},
                      neg={OWLNamedIndividual("http://dbpedia.org/resource/Barack_Obama")})
# (5) Learn description logic concepts best fitting (4).
h = model.fit(learning_problem=lp).best_hypotheses()
print(h)
print(owl_expression_to_dl(h))
print(owl_expression_to_sparql(expression=h))
save_owl_class_expressions(expressions=h,path="owl_prediction")

Fore more please refer to the examples folder.

ontolearn-webservice

ontolearn-webservice --path_knowledge_base KGs/Mutagenesis/mutagenesis.owl

ontolearn-webservice --endpoint_triple_store http://0.0.0.0:9080/sparql

import json
import requests
with open(f"LPs/Mutagenesis/lps.json") as json_file:
    settings = json.load(json_file)
for str_target_concept, examples in settings['problems'].items():
    response = requests.get('http://0.0.0.0:8000/cel',
                            headers={'accept': 'application/json', 'Content-Type': 'application/json'},
                            json={ "pos": examples['positive_examples'],
                                   "neg": examples['negative_examples'],
                                   "model": "Drill",
                                   "path_embeddings": "mutagenesis_embeddings/Keci_entity_embeddings.csv",
                                   "num_of_training_learning_problems": 2,
                                   "num_of_target_concepts":3,
                                   "max_runtime": 10, # seconds
                                   "iter_bound": 100 # number of iterations/applied refinement opt.
                                   })
    print(response.json())# {'Prediction': '∀ hasStructure.(¬Hetero_aromatic_5_ring)'}

import json
import requests
with open(f"LPs/Mutagenesis/lps.json") as json_file:
    settings = json.load(json_file)
for str_target_concept, examples in settings['problems'].items():
    response = requests.get('http://0.0.0.0:8000/cel',
                            headers={'accept': 'application/json', 'Content-Type': 'application/json'}, json={
            "pos": examples['positive_examples'],
            "neg": examples['negative_examples'],
            "model": "Drill",
            "path_embeddings": "mutagenesis_embeddings/Keci_entity_embeddings.csv",
            "pretrained":"pretrained",
            "max_runtime": 10,
            "iter_bound": 100,
        })
    print(response.json())

Benchmark Results

# To download learning problems. # Benchmark learners on the Family benchmark dataset with benchmark learning problems.
wget https://files.dice-research.org/projects/Ontolearn/LPs.zip -O ./LPs.zip && unzip LPs.zip

# To download learning problems and benchmark learners on the Family benchmark dataset with benchmark learning problems.
python examples/concept_learning_evaluation.py --lps LPs/Family/lps.json --kb KGs/Family/family-benchmark_rich_background.owl --max_runtime 60 --report family_results.csv  && python -c 'import pandas as pd; print(pd.read_csv("family_results.csv", index_col=0).to_markdown(floatfmt=".3f"))'

python examples/concept_learning_cv_evaluation.py --path_of_nces_embeddings NCESData/mutagenesis/embeddings/ConEx_entity_embeddings.csv --path_of_clip_embeddings CLIPData/mutagenesis/embeddings/ConEx_entity_embeddings.csv --folds 10 --kb KGs/Mutagenesis/mutagenesis.owl --lps LPs/Mutagenesis/lps.json --max_runtime 60 --report mutagenesis_results.csv && python -c 'import pandas as pd; print(pd.read_csv("mutagenesis_results.csv", index_col=0).to_markdown(floatfmt=".3f"))'

python examples/concept_learning_cv_evaluation.py --path_of_nces_embeddings NCESData/carcinogenesis/embeddings/ConEx_entity_embeddings.csv --path_of_clip_embeddings CLIPData/carcinogenesis/embeddings/ConEx_entity_embeddings.csv --folds 10 --kb KGs/Carcinogenesis/carcinogenesis.owl --lps LPs/Carcinogenesis/lps.json --max_runtime 60 --report carcinogenesis_results.csv && python -c 'import pandas as pd; print(pd.read_csv("carcinogenesis_results.csv", index_col=0).to_markdown(floatfmt=".3f"))'

Deployment

pip install gradio # (check `pip show gradio` first)

Available models to deploy: EvoLearner, NCES, CELOE and OCEL. To deploy EvoLearner on the Family knowledge graph:

python deploy_cl.py --model evolearner --path_knowledge_base KGs/Family/family-benchmark_rich_background.owl

Run the help command to see the description on this script usage:

python deploy_cl.py --help

Development

Creating a feature branch refactoring from development branch

git branch refactoring develop

Citing

Currently, we are working on our manuscript describing our framework. If you find our work useful in your research, please consider citing the respective paper:

# DRILL
@inproceedings{demir2023drill,
  author = {Demir, Caglar and Ngomo, Axel-Cyrille Ngonga},
  booktitle = {The 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023},
  title = {Neuro-Symbolic Class Expression Learning},
  url = {https://www.ijcai.org/proceedings/2023/0403.pdf},
 year={2023}
}

# NCES2
@inproceedings{kouagou2023nces2,
author={Kouagou, N'Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille},
title={Neural Class Expression Synthesis in ALCHIQ(D)},
url = {https://papers.dice-research.org/2023/ECML_NCES2/NCES2_public.pdf},
booktitle={Machine Learning and Knowledge Discovery in Databases},
year={2023},
publisher={Springer Nature Switzerland},
address="Cham"
}

# NCES
@inproceedings{kouagou2023neural,
  title={Neural class expression synthesis},
  author={Kouagou, N’Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille},
  booktitle={European Semantic Web Conference},
  pages={209--226},
  year={2023},
  publisher={Springer Nature Switzerland}
}

# EvoLearner
@inproceedings{heindorf2022evolearner,
  title={Evolearner: Learning description logics with evolutionary algorithms},
  author={Heindorf, Stefan and Bl{\"u}baum, Lukas and D{\"u}sterhus, Nick and Werner, Till and Golani, Varun Nandkumar and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille},
  booktitle={Proceedings of the ACM Web Conference 2022},
  pages={818--828},
  year={2022}
}


# CLIP
@inproceedings{kouagou2022learning,
  title={Learning Concept Lengths Accelerates Concept Learning in ALC},
  author={Kouagou, N’Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille},
  booktitle={European Semantic Web Conference},
  pages={236--252},
  year={2022},
  publisher={Springer Nature Switzerland}
}

In case you have any question, please contact: [email protected] or [email protected]