ontolearn 0.7.1

ontolearn 0.7.1 is now released!

pip install -U ontolearn

Important Updates: ontolearn-webservice

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

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

What's Changed

• Refactoring by @Demirrr in #354
• Examples Clean Up by @alkidbaci in #353
• Last commit of refactoring DRILL by @Demirrr in #356
• Nominals fix by @Demirrr in #358
• LLM based verbalizer included by @Demirrr in #360
• Prompt is revised to lead an LLM to generate shorter texts. by @Demirrr in #361
• TripleStore via rdflib.graph by @Demirrr in #364
• Tdl triplestore by @Demirrr in #365
• Tdl triplestore by @Demirrr in #367
• OWL Class expression learning with tDL, over a DBpedia Endpoint by @Demirrr in #368
• python dependencies are removed in the github action for docs by @Demirrr in #369
• Unifying best_hypotheses function and updating the tests by @Demirrr in #370
• Update README.md by @Demirrr in #371
• Release refactoring by @Demirrr in #373
• Drill Enexa Server by @Demirrr in #375
• Fixing few open issues by @Demirrr in #378
• Adaptation to owlapy1.0.1 by @alkidbaci in #379
• Fix:Drill: No embeddings provided implies Quality based reward used by @Demirrr in #380
• Evaluation setup for NCES and CLIP by @alkidbaci in #382
• ontolearn-webservice with drill examples over local kg tested by @Demirrr in #384
• tDL, DRILL, Triplestore Fuseki refactoring by @Demirrr in #386
• Fix data properties drill tdl by @Demirrr in #388
• Refactoring by @alkidbaci in #390
• Tentris drill tdl refactoring by @Demirrr in #391
• License update by @alkidbaci in #392
• Making ontolearn-webservice more responsive by @Demirrr in #393
• webservice fix is done by @Demirrr in #394
• Readme updated by
  and <\details> by @Demirrr in #395

Full Changelog: 0.7.0...0.7.1

ontolearn 0.7.0

ontolearn 0.7.0 is now released!

Release Notes:

Drill is now available in Ontolearn:

You can import it as follows:

from ontolearn.learners import Drill

Examples:

1. examples/concept_learning_evaluation.py
2. examples/concept_learning_cv_evaluation.py

---

Tree-based DL Learner (tDL) is now available in Ontolearn:

You can import it as follows:

from ontolearn.learners import TDL

Examples:

1. examples/concept_learning_evaluation.py
2. examples/concept_learning_cv_evaluation.py
3. examples/concept_learning_with_tdl_and_triplestore_kb.py

---

CLIP is now available in Ontolearn:

You can import it as follows:

from ontolearn.concept_learner import  CLIP

Examples:

1. examples/concept_learning_cv_evaluation.py

---

Changes to KnowledgeBase class:

• You can make type retrieval methods to return the type of OWLNamedIndividual for individuals which do not explicitly specify that type. You can do that by setting the argument include_implicit_individuals of class KnowledgeBase to True. By default it is False.

• Ontology and reasoner can be accessed directly:

  • From kb.ontology() → To kb.ontology
  • From kb.reasoner() → To kb.reasoner

• Added methods for triple retrieval:

  • abox → returns all related Abox axioms of a given individual, list of individuals or None (all Abox axioms).
  • tbox → method returns all related Tbox axioms of a given concept, data property, object property, a list of them or None (all Tbox axioms)
  • triples → returns all triples of the ontology.

  Return type in 3 formats defined by the mode argument which accepts the following strings:
  1) 'native' -> triples are represented as tuples of owlapy objects.
  2) 'iri' -> triples are represented as tuples of IRIs as strings.
  3) 'axiom' -> triples are represented as owlapy axioms.

• New property methods to retrieve classes/properties:

  • concepts
  • object_properties
  • object_properties

• Removed triplestore logic (as well as from OWLOntology_Owlready2 and OWLReasoner_Owlready2). It is now moved to ontolearn.triple_store (described below).

Check everything here

---

Triple Store Knowledge Base:

Added TripleStoreOntology, TripleStoreReasoner and TripleStoreKnowledgeBase.
TripleStoreKnowledgeBase can be initialized using just an SPARQL endpoint and it can be used instead of the KnowledgeBase
to execute a concept learner. All dataset queries are made using SPARQL and are directed to the provided endpoint.

To import:

from ontolearn.triple_store import TripleStoreOntology, TripleStoreReasoner, TripleStoreKnowledgeBase

For more, you can visit the guide in our documentation here , check the API docs and see the examples listed below.

Examples:

1. examples/concept_learning_via_triplestore_example.py
2. examples/concept_learning_with_tdl_and_triplestore_kb.py

---

Documentation and more:

• At README.md you can find the Benchmark Results which displays the performance of all our learners.

• Documentation has been updated to the latest changes. You can always access the up-to-date documentation here.

• Ontosample is now integrated into Ontolearn. We have also added a guide on how to use it as well as an example.

  Note: ontosample is not part of the default dependencies. To get it you should either install it directly or use: pip install ontolearn[full].

---

Changes on dependencies:

• We have added some new dependencies and increased the minimum required version for some of them.
• Some dependencies are made optional. You can now install all of them or just the minimum required ones.
  • pip install ontolearn[min] → the default one when you execute pip install ontolearn
  • pip install ontolearn[full] → to install the extra dependenices.
    You can check them here.

---

Bug Fixes and others:

• Fixed a bug where using the same EvoLearner model to fit more than one learning problem would cause quality drop.
• Added learning problem generator as Python module
• Other minor changes that in case you are interested, you can check the PRs comments.

---

As always you can upgrade with pip:

pip install -U ontolearn

Brought to you by Ontolearn Team.

ontolearn 0.6.1

ontolearn 0.6.1

We're happy to announce the 0.6.1 release.

You can upgrade with pip as usual:

pip install -U ontolearn

ontolearn 0.5.4

ontolearn 0.5.4

We're happy to announce the 0.5.4 release.

You can upgrade with pip as usual:

pip install -U ontolearn

ontolearn 0.5.3

ontolearn 0.5.3

We're happy to announce the 0.5.3 release.

You can upgrade with pip as usual:

pip install -U ontolearn

First Release of Ontolearn

Features

• Properly check domain inclusion in the ConceptGenerator
• Add Top-Level CNF/DNF conversion

Fixes

• Fix OCEL (still not equivalent of the DL-Learner implementation)
• Fix a bug in the DLSyntaxParser to correctly parse Thing/Nothing
• Multiple fits for EvoLearner on datasets with data properties
• Correctly filter super properties in the OWLReasoner_Owlready2

Maintenance

• Use closed world behaviour for negations per default (FastInstanceChecker)

Todos for the next release

• Integrate (https://gradio.app/quickstart/) as done in https://github.com/dice-group/dice-embeddings. By this, we can increase the usability of our framework (CD).
• Remove ontolearn/endpoint as an endpoint does not belong to ontolearn but a particular application of it (CD).
• Update https://ontolearn-docs-dice-group.netlify.app/ (CD).
• Allow classification of new individuals that are not part of the existing knowledge base (#213, #233) (LB).
• Add support for sub-properties to the FastInstanceChecker/OWLReasoner_Owlready2 (as an option) (LB).