small consistency fixes

application/AccessAQM.py

@@ -22,7 +22,6 @@ co = rdflib.URIRef("http://www.semanticweb.org/beyersdo/ontologies/2022/10/AQM#C
 qa = rdflib.URIRef("http://www.semanticweb.org/beyersdo/ontologies/2022/10/AQM#QualityAttribute")
 vf = rdflib.URIRef("http://www.semanticweb.org/beyersdo/ontologies/2022/10/AQM#VariationFactor")
 
-
 purposes = [a, c, es, im, ma, me, mo, pr, sp]
 measurement_purposes = [a, c, es, im, ma, me, mo, pr]
 
@@ -48,22 +47,25 @@ def getAQM(conversion=True):
 def createAQMSubgraphForConsensus(source, conversion=True):
     """
         This function shall enable the analysis of semantic equivalence of one quality model (source) with the others in the AQM.
+        Thereby, different understandings about quality models are aligned resulting in a consensus about quality.
+        If the AQM contains domain standards with regulations on the desired quality, the fulfillment of this regulations can be verified.
+        
         A subgraph of the AQM is needed, containing the requested quality model (source) and quality attributes of other quality models,
         that are aligned via the isEquivalent object property. 
         
         Note: The source needs to be a string representation of the annotation property "Citation" in the AQM (currently in AlphaBixTex format)
     """
     
-    #Load the current backup of the AQM and create an empty subgraph
+    #Load current backup of the AQM and create empty subgraph
     aqm = rdflib.Graph(base="http://www.semanticweb.org/beyersdo/ontologies/2022/10/AQM#")
     aqm.parse(os.path.join(str(os.getcwd()) + "\\application\\static\\data\\AQM.owl"))
 
     subgraph = rdflib.Graph()
     
-    #Iterate through all triples with the annotation property "Citation" and having the value of the requested source.
+    #Iterate through all triples with the annotation property "Citation" having the value of the requested source.
     for s, p, o in aqm.triples((None, ci, rdflib.Literal(source, lang="en"))):
         
-        subgraph.add((s,p,o)) #subgraph now contains all quality attributes / variation factors of the requested quality model
+        subgraph.add((s,p,o)) #subgraph now contains all quality attributes of the requested quality model
         
         #For given quality attribute / variation factor, add properties (e.g. subClassOf or annotations)
         for s1, p1, o1 in aqm.triples((s, None, None)):
@@ -185,7 +187,6 @@ def createAQMSubgraphForMeasurement(source):
                 
                 #Check if object property has range in measurement_purposes
                 if o2 in measurement_purposes:
-                    print(str(s2))
                     subgraph.add((s2, p2, o2))
                     subgraph.add((s1, p1, o1))