use single script for dbpedia concept learning with drill and tdl; up…

…date README

README.md

@@ -121,15 +121,15 @@ save_owl_class_expressions(expressions=h,path="owl_prediction")
 - With one command
 1. For TDL
 ```bash
-git clone https://github.com/dice-group/Ontolearn.git && cd Ontolearn && git checkout large_scale_cel && conda create -n venv python=3.10.14 --no-default-packages --y && conda activate venv && pip install -e . && wget https://files.dice-research.org/projects/Ontolearn/LPs.zip -O ./LPs.zip && unzip LPs.zip && python examples/dbpedia_concept_learning_with_tdl_and_triplestore.py
+git clone https://github.com/dice-group/Ontolearn.git && cd Ontolearn && git checkout large_scale_cel && conda create -n venv python=3.10.14 --no-default-packages --y && conda activate venv && pip install -e . && wget https://files.dice-research.org/projects/Ontolearn/LPs.zip -O ./LPs.zip && unzip LPs.zip && python examples/dbpedia_concept_learning_with_ontolearn.py tdl
 ```
 2. For Drill
 ```bash
-git clone https://github.com/dice-group/Ontolearn.git && cd Ontolearn && git checkout large_scale_cel && conda create -n venv python=3.10.14 --no-default-packages --y && conda activate venv && pip install -e . && wget https://files.dice-research.org/projects/Ontolearn/LPs.zip -O ./LPs.zip && unzip LPs.zip && python examples/dbpedia_concept_learning_with_drill_and_triplestore.py
+git clone https://github.com/dice-group/Ontolearn.git && cd Ontolearn && git checkout large_scale_cel && conda create -n venv python=3.10.14 --no-default-packages --y && conda activate venv && pip install -e . && wget https://files.dice-research.org/projects/Ontolearn/LPs.zip -O ./LPs.zip && unzip LPs.zip && python examples/dbpedia_concept_learning_with_ontolearn.py drill
 ```
 3. For DL-Learner
 ```bash
-git clone https://github.com/dice-group/Ontolearn.git && cd Ontolearn && git checkout large_scale_cel && conda create -n venv python=3.10.14 --no-default-packages --y && conda activate venv && pip install -e . && https://github.com/SmartDataAnalytics/DL-Learner/releases/download/1.4.0/dllearner-1.4.0.zip -O ./dllearner-1.4.0.zip && unzip dllearner-1.4.0.zip && wget https://files.dice-research.org/projects/Ontolearn/LPs.zip -O ./LPs.zip && unzip LPs.zip && python examples/dbpedia_dl_learner_sparql.py
+git clone https://github.com/dice-group/Ontolearn.git && cd Ontolearn && git checkout large_scale_cel && conda create -n venv python=3.10.14 --no-default-packages --y && conda activate venv && pip install -e . && wget https://github.com/SmartDataAnalytics/DL-Learner/releases/download/1.4.0/dllearner-1.4.0.zip -O ./dllearner-1.4.0.zip && unzip dllearner-1.4.0.zip && wget https://files.dice-research.org/projects/Ontolearn/LPs.zip -O ./LPs.zip && unzip LPs.zip && python examples/dbpedia_concept_learning_with_dllearner.py
 ```
 
 Fore more please refer to  the [examples](https://github.com/dice-group/Ontolearn/tree/develop/examples) folder.

examples/dbpedia_concept_learning_with_drill_and_triplestore.py → examples/dbpedia_concept_learning_with_ontolearn.py

@@ -1,28 +1,36 @@
-import json, os
+import json, os, sys
 from owlapy.owl_individual import OWLNamedIndividual, IRI
-from ontolearn.learners import Drill
+from ontolearn.learners import Drill, TDL
 from ontolearn.learning_problem import PosNegLPStandard
 from ontolearn.triple_store import TripleStore
 from ontolearn.utils.static_funcs import save_owl_class_expressions
 from owlapy.render import DLSyntaxObjectRenderer
-# (1) Initialize Triplestore
+
+if len(sys.argv) < 2:
+    print("You need to provide the model name; either tdl or drill")
+    sys.exit(1)
+
+model_name = sys.argv[1]
+assert model_name.lower() in ["drill", "tdl"], "Currently, only Drill and TDL are supported"
+
+# (1) Initialize knowledge source with TripleStore
 kb = TripleStore(url="https://dbpedia.data.dice-research.org/sparql")
 # (2) Initialize a DL renderer.
 renderer = DLSyntaxObjectRenderer()
 # (3) Initialize a learner.
-model = Drill(knowledge_base=kb, max_runtime=240)
+model = Drill(knowledge_base=kb, max_runtime=240) if model_name.lower() == "drill" else TDL(knowledge_base=kb)
 # (4) Solve learning problems
 with open("./LPs/DBpedia2022-12/lps.json") as f:
     lps = json.load(f)
 for i, item in enumerate(lps):
     print("\nTarget expression: ", item["target expression"], "\n")
     lp = PosNegLPStandard(pos=set(list(map(OWLNamedIndividual,map(IRI.create, item["examples"]["positive examples"])))),
                           neg=set(list(map(OWLNamedIndividual,map(IRI.create, item["examples"]["negative examples"])))))
-    # (5) Learn description logic concepts best fitting (4).
+    # (5) Learn description logic concepts best fitting
     h = model.fit(learning_problem=lp).best_hypotheses()
     str_concept = renderer.render(h)
     print("Concept:", str_concept)  # e.g.  ∃ predecessor.WikicatPeopleFromBerlin
-    # (6) Save ∃ predecessor.WikicatPeopleFromBerlin into disk
-    if not os.path.exists("./learned_owl_expressions_drill"):
-        os.mkdir("./learned_owl_expressions_drill")
-    save_owl_class_expressions(expressions=h, path=f"./learned_owl_expressions_drill/owl_prediction_{i}")
+    # (6) Save e.g., ∃ predecessor.WikicatPeopleFromBerlin into disk
+    if not os.path.exists(f"./learned_owl_expressions_{model_name}"):
+        os.mkdir(f"./learned_owl_expressions_{model_name}")
+    save_owl_class_expressions(expressions=h, path=f"./learned_owl_expressions_{model_name}/owl_prediction_{i}")

examples/generate_lps_dbpedia.py

@@ -0,0 +1,11 @@
+from ontolearn.lp_generator import LPGen
+
+PATH = 'https://dbpedia.data.dice-research.org/sparql'
+STORAGE_DIR = 'DBpedia_LPs'
+
+def generate_lps():
+    lp_gen = LPGen(kb_path=PATH, storage_dir=STORAGE_DIR, refinement_expressivity=1e-7, use_triple_store=True, sample_fillers_count=1, num_sub_roots=1)
+    lp_gen.generate()
+
+if __name__ == '__main__':
+    generate_lps()