ontolearn 0.7.1

Ontolearn is an open-source software library for structured machine learning in Python. Ontolearn includes modules for processing knowledge bases, inductive logic programming and ontology engineering.

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

Click me!

Load an RDF knowledge graph

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

or launch a Tentris instance https://github.com/dice-group/tentris over Mutagenesis.

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

The below code trains DRILL with 6 randomly generated learning problems provided that path_to_pretrained_drill does not lead to a directory containing pretrained DRILL. Thereafter, trained DRILL is saved in the directory path_to_pretrained_drill. Finally, trained DRILL will learn an OWL class expression.

import json
import requests
with open(f"LPs/Mutagenesis/lps.json") as json_file:
    learning_problems = json.load(json_file)["problems"]
for str_target_concept, examples in learning_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",
                                  "path_to_pretrained_drill": "pretrained_drill",
                                  # if pretrained_drill exists, upload, otherwise train one and save it there
                                  "num_of_training_learning_problems": 2,
                                  "num_of_target_concepts": 3,
                                  "max_runtime": 60000,  # seconds
                                  "iter_bound": 1  # number of iterations/applied refinement opt.
                                  })
    print(response.json())  # {'Prediction': '∀ hasAtom.(¬Nitrogen-34)', 'F1': 0.7283582089552239, 'saved_prediction': 'Predictions.owl'}

TDL (a more scalable learner) can also be used as follows

import json
import requests
with open(f"LPs/Mutagenesis/lps.json") as json_file:
    learning_problems = json.load(json_file)["problems"]
for str_target_concept, examples in learning_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": "TDL"})
    print(response.json())

Benchmark Results

To see the 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"))'

Below, we report the average results of 5 runs. Each model has 60 second to find a fitting answer. DRILL results are obtained by using F1 score as heuristic function. Note that F1 scores denote the quality of the find/constructed concept w.r.t. E^+ and E^-.

Family Benchmark Results

LP	Train-F1-OCEL	Test-F1-OCEL	RT-OCEL	Train-F1-CELOE	Test-F1-CELOE	RT-CELOE	Train-F1-Evo	Test-F1-Evo	RT-Evo	Train-F1-DRILL	Test-F1-DRILL	RT-DRILL	Train-F1-TDL	Test-F1-TDL	RT-TDL	Train-F1-NCES	Test-F1-NCES	RT-NCES	Train-F1-CLIP	Test-F1-CLIP	RT-CLIP
Aunt	0.848	0.637	9.206	0.918	0.855	9.206	0.996	0.969	3.390	0.886	0.799	60.243	0.971	0.949	6.366	0.721	0.635	0.552	0.899	0.891	5.763
Brother	1.000	1.000	0.005	1.000	1.000	0.005	1.000	1.000	0.281	1.000	1.000	0.020	1.000	1.000	6.216	0.978	0.975	0.450	1.000	1.000	0.692
Cousin	0.740	0.708	7.336	0.796	0.789	7.336	1.000	1.000	1.653	0.831	0.784	60.416	0.978	0.941	7.073	0.667	0.667	0.465	0.774	0.761	6.671
Daughter	1.000	1.000	0.006	1.000	1.000	0.006	1.000	1.000	0.309	1.000	1.000	0.033	1.000	1.000	6.459	0.993	0.977	0.534	1.000	1.000	0.716
Father	1.000	1.000	0.002	1.000	1.000	0.002	1.000	1.000	0.411	1.000	1.000	0.004	1.000	1.000	6.522	0.897	0.903	0.448	1.000	1.000	0.588
Granddaughter	1.000	1.000	0.002	1.000	1.000	0.002	1.000	1.000	0.320	1.000	1.000	0.003	1.000	1.000	6.233	0.911	0.916	0.497	1.000	1.000	0.646
Grandfather	1.000	1.000	0.002	1.000	1.000	0.002	1.000	1.000	0.314	1.000	1.000	0.003	1.000	1.000	6.185	0.743	0.717	0.518	1.000	1.000	0.721
Grandgranddaughter	1.000	1.000	0.004	1.000	1.000	0.004	1.000	1.000	0.293	1.000	1.000	0.002	1.000	1.000	5.858	0.837	0.840	0.518	1.000	1.000	0.710
Grandgrandfather	1.000	1.000	0.668	1.000	1.000	0.668	1.000	1.000	0.341	1.000	1.000	0.243	0.951	0.947	5.915	0.759	0.677	0.511	1.000	1.000	1.964
Grandgrandmother	1.000	1.000	0.381	1.000	1.000	0.381	1.000	1.000	0.258	1.000	1.000	0.243	0.944	0.947	5.918	0.721	0.687	0.498	0.997	1.000	2.620
Grandgrandson	1.000	1.000	0.341	1.000	1.000	0.341	1.000	1.000	0.276	1.000	1.000	0.122	0.938	0.911	6.093	0.779	0.809	0.460	1.000	1.000	2.555
Grandmother	1.000	1.000	0.002	1.000	1.000	0.002	1.000	1.000	0.385	1.000	1.000	0.003	1.000	1.000	6.135	0.762	0.725	0.480	1.000	1.000	0.628
Grandson	1.000	1.000	0.002	1.000	1.000	0.002	1.000	1.000	0.299	1.000	1.000	0.003	1.000	1.000	6.301	0.896	0.903	0.552	1.000	1.000	0.765
Mother	1.000	1.000	0.002	1.000	1.000	0.002	1.000	1.000	0.327	1.000	1.000	0.004	1.000	1.000	6.570	0.967	0.972	0.555	1.000	1.000	0.779
PersonWithASibling	1.000	1.000	0.002	1.000	1.000	0.002	1.000	1.000	0.377	0.737	0.725	60.194	1.000	1.000	6.548	0.927	0.928	0.648	1.000	1.000	0.999
Sister	1.000	1.000	0.002	1.000	1.000	0.002	1.000	1.000	0.356	1.000	1.000	0.017	1.000	1.000	6.315	0.866	0.876	0.512	1.000	1.000	0.616
Son	1.000	1.000	0.002	1.000	1.000	0.002	1.000	1.000	0.317	1.000	1.000	0.004	1.000	1.000	6.579	0.892	0.855	0.537	1.000	1.000	0.700
Uncle	0.903	0.891	12.441	0.907	0.891	12.441	1.000	0.971	1.675	0.951	0.894	60.337	0.894	0.896	6.310	0.667	0.665	0.619	0.928	0.942	5.577

Mutagenesis Benchmark Results

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"))'

LP	Train-F1-OCEL	Test-F1-OCEL	RT-OCEL	Train-F1-CELOE	Test-F1-CELOE	RT-CELOE	Train-F1-Evo	Test-F1-Evo	RT-Evo	Train-F1-DRILL	Test-F1-DRILL	RT-DRILL	Train-F1-TDL	Test-F1-TDL	RT-TDL	Train-F1-NCES	Test-F1-NCES	RT-NCES	Train-F1-CLIP	Test-F1-CLIP	RT-CLIP
NotKnown	0.916	0.918	58.328	0.916	0.918	58.328	0.724	0.729	49.281	0.704	0.704	60.052	0.879	0.771	7.763	0.564	0.560	0.493	0.814	0.807	5.622

Carcinogenesis Benchmark Results

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"))'

LP	Train-F1-OCEL	Test-F1-OCEL	RT-OCEL	Train-F1-CELOE	Test-F1-CELOE	RT-CELOE	Train-F1-Evo	Test-F1-Evo	RT-Evo	Train-F1-DRILL	Test-F1-DRILL	RT-DRILL	Train-F1-TDL	Test-F1-TDL	RT-TDL	Train-F1-NCES	Test-F1-NCES	RT-NCES	Train-F1-CLIP	Test-F1-CLIP	RT-CLIP
NOTKNOWN	0.738	0.711	42.936	0.740	0.701	42.936	0.744	0.733	63.465	0.705	0.704	60.069	0.879	0.682	7.260	0.415	0.396	1.911	0.720	0.700	85.037

Use python examples/concept_learning_cv_evaluation.py to apply stratified k-fold cross validation on learning problems.

Deployment

To see the results

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

To see the results

Creating a feature branch refactoring from development branch

git branch refactoring develop

References

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: [e-mail] or [e-mail]