def createTestOntGraph(): graph = Graph() graph.bind('ex', EX_NS, True) Individual.factoryGraph = graph kneeJoint = EX_CL.KneeJoint joint = EX_CL.Joint knee = EX_CL.Knee isPartOf = Property(EX_NS.isPartOf) graph.add((isPartOf.identifier, RDF.type, OWL_NS.TransitiveProperty)) structure = EX_CL.Structure leg = EX_CL.Leg hasLocation = Property(EX_NS.hasLocation, subPropertyOf=[isPartOf]) # graph.add((hasLocation.identifier,RDFS.subPropertyOf,isPartOf.identifier)) kneeJoint.equivalentClass = [joint & (isPartOf | some | knee)] legStructure = EX_CL.LegStructure legStructure.equivalentClass = [structure & (isPartOf | some | leg)] structure += leg structure += joint locatedInLeg = hasLocation | some | leg locatedInLeg += knee # log.debug(graph.serialize(format='n3')) # newGraph = Graph() # newGraph.bind('ex',EX_NS,True) # newGraph,conceptMap = StructuralTransformation(graph,newGraph) # revDict = dict([(v,k) for k,v in conceptMap.items()]) # Individual.factoryGraph = newGraph # for oldConceptId ,newConceptId in conceptMap.items(): # if isinstance(oldConceptId,BNode): # oldConceptRepr = repr(Class(oldConceptId,graph=graph)) # if oldConceptRepr.strip() == 'Some Class': # oldConceptRepr = manchesterSyntax( # oldConceptId, # graph) # log.debug("%s -> %s" % ( # oldConceptRepr, # newConceptId # )) # else: # log.debug("%s -> %s"%( # oldConceptId, # newConceptId # )) # for c in AllClasses(newGraph): # if isinstance(c.identifier,BNode) and c.identifier in conceptMap.values(): # log.debug("## %s ##" % c.identifier) # else: # log.debug("##" * 10) # log.debug(c.__repr__(True)) # log.debug("################################") return graph
def test_manchester_owl_restrictions(self): # Restrictions can also be created using Manchester OWL syntax in # 'colloquial' Python. A Python infix operator recipe was used for # this purpose. See below assert pformat( exNs.hasParent | some | Class(exNs.Physician, graph=self.graph)) == \ '( ex:hasParent SOME ex:Physician )', pformat(exNs.hasParent | some | Class(exNs.Physician, graph=self.graph)) assert pformat( Property(exNs.hasParent, graph=self.graph) | max | Literal(1)) == \ '( ex:hasParent MAX 1 )', pformat(Property(exNs.hasParent, graph=self.graph) | max | Literal(1))
def testGeneralConceptInclusion(self): # Some Class # ## Primitive Type ## # SubClassOf: Class: ex:NoExclusion . # DisjointWith ( ex:contains some ex:IsolatedCABGConcomitantExclusion ) contains = Property(EX_NS.contains) testClass = ~(contains | some | EX.Exclusion) testClass2 = EX.NoExclusion testClass2 += testClass NormalFormReduction(self.ontGraph) individual1 = BNode() individual2 = BNode() contains.extent = [(individual1, individual2)] ruleStore, ruleGraph, network = SetupRuleStore(makeNetwork=True) posRules, negRules = CalculateStratifiedModel(network, self.ontGraph, [EX_NS.NoExclusion]) self.failUnless(not posRules, "There should be no rules in the 0 strata.") self.assertEqual(len(negRules), 2, "There should be 2 'negative' rules") Individual.factoryGraph = network.inferredFacts targetClass = Class(EX_NS.NoExclusion, skipOWLClassMembership=False) self.failUnless( individual1 in targetClass.extent, "There is a BNode that bears the contains relation with another individual that is not a member of Exclusion." ) self.assertEquals(len(list(targetClass.extent)), 1, "There should only be one member in NoExclusion")
def testNominalPartition(self): partition = EnumeratedClass( EX_NS.part, members=[EX_NS.individual1, EX_NS.individual2, EX_NS.individual3]) subPartition = EnumeratedClass(members=[EX_NS.individual1]) partitionProp = Property(EX_NS.propFoo, range=partition.identifier) self.testClass = (EX.Bar) & (partitionProp | only | subPartition) self.testClass.identifier = EX_NS.Foo self.assertEqual(repr(self.testClass), 'ex:Bar THAT ( ex:propFoo ONLY { ex:individual1 } )') self.assertEqual(repr(self.testClass.identifier), "rdflib.term.URIRef(u'http://example.com/Foo')") NormalFormReduction(self.ontGraph) self.assertEqual( repr(self.testClass), "ex:Bar that ( not ( ex:propFoo value ex:individual2 ) ) and ( not ( ex:propFoo value ex:individual3 ) )" ) ruleStore, ruleGraph, network = SetupRuleStore(makeNetwork=True) ex = BNode() (EX.Bar).extent = [ex] self.ontGraph.add((ex, EX_NS.propFoo, EX_NS.individual1)) CalculateStratifiedModel(network, self.ontGraph, [EX_NS.Foo]) self.failUnless((ex, RDF.type, EX_NS.Foo) in network.inferredFacts, "Missing level 1 predicate (ex:Foo)")
def testNegatedDisjunctionTest(self): contains = Property(EX_NS.contains) omega = EX.Omega alpha = EX.Alpha innerDisjunct = omega | alpha foo = EX.foo testClass1 = foo & (contains | only | ~innerDisjunct) testClass1.identifier = EX_NS.Bar self.assertEqual( repr(testClass1), 'ex:foo THAT ( ex:contains ONLY ( NOT ( ex:Omega OR ex:Alpha ) ) )' ) NormalFormReduction(self.ontGraph) self.assertEqual( repr(testClass1), 'ex:foo THAT ( NOT ( ex:contains SOME ( ex:Omega OR ex:Alpha ) ) )' ) individual1 = BNode() individual2 = BNode() foo.extent = [individual1] contains.extent = [(individual1, individual2)] (EX.Baz).extent = [individual2] ruleStore, ruleGraph, network = SetupRuleStore(makeNetwork=True) posRules, ignored = CalculateStratifiedModel(network, self.ontGraph, [EX_NS.Bar]) self.failUnless(not posRules, "There should be no rules in the 0 strata.") self.assertEqual(len(ignored), 2, "There should be 2 'negative' rules") testClass1.graph = network.inferredFacts self.failUnless(individual1 in testClass1.extent, "%s should be in ex:Bar's extent" % individual1)
def transform(self, graph): """ Transforms a universal restriction on a 'pure' nominal range into a conjunction of value restriction (using set theory and demorgan's laws) """ Individual.factoryGraph = graph for restriction, intermediateCl, nominal, prop, partition in graph.query( self.NOMINAL_QUERY, initNs={ u'owl': OWL_NS, u'rdfs': str(RDFS) }): exceptions = EnumeratedClass() partition = Collection(graph, partition) nominalCollection = Collection(graph, nominal) for i in partition: if i not in nominalCollection: exceptions._rdfList.append(i) #exceptions+=i exists = Class(complementOf=(Property(prop) | some | exceptions)) for s, p, o in graph.triples((None, None, restriction)): graph.add((s, p, exists.identifier)) Individual(restriction).delete() #purge nominalization placeholder iClass = BooleanClass(intermediateCl) iClass.clear() iClass.delete()
def testExistentialInRightOfGCI(self): someProp = Property(EX_NS.someProp) existential = someProp | some | EX.Omega existential += EX.Foo self.assertEqual( repr(Class(EX_NS.Foo)), "Class: ex:Foo SubClassOf: ( ex:someProp SOME ex:Omega )") ruleStore, ruleGraph, network = SetupRuleStore(makeNetwork=True)
def testUniversalInversion(self): testClass1 = EX.omega & (Property(EX_NS.someProp) | only | ~EX.gamma) testClass1.identifier = EX_NS.Foo self.assertEquals(repr(testClass1), 'ex:omega THAT ( ex:someProp ONLY ( NOT ex:gamma ) )') NormalFormReduction(self.ontGraph) self.assertEquals(repr(testClass1), 'ex:omega THAT ( NOT ( ex:someProp SOME ex:gamma ) )')
def testOtherForm(self): contains = Property(EX_NS.contains) locatedIn = Property(EX_NS.locatedIn) topConjunct = (EX.Cath & (contains | some | (EX.MajorStenosis & (locatedIn | value | EX_NS.LAD))) & (contains | some | (EX.MajorStenosis & (locatedIn | value | EX_NS.RCA)))) (EX.NumDisV2D) += topConjunct from FuXi.DLP.DLNormalization import NormalFormReduction NormalFormReduction(self.ontGraph) ruleStore, ruleGraph, network = SetupRuleStore(makeNetwork=True) rules = network.setupDescriptionLogicProgramming( self.ontGraph, derivedPreds=[EX_NS.NumDisV2D], addPDSemantics=False, constructNetwork=False) from FuXi.Rete.Magic import PrettyPrintRule for rule in rules: PrettyPrintRule(rule)
def setUp(self): self.ontGraph = Graph() self.ontGraph.bind('ex', EX_NS) self.ontGraph.bind('owl', OWL_NS) Individual.factoryGraph = self.ontGraph partition = EnumeratedClass( EX_NS.part, members=[EX_NS.individual1, EX_NS.individual2, EX_NS.individual3]) subPartition = EnumeratedClass(EX_NS.partition, members=[EX_NS.individual1]) partitionProp = Property(EX_NS.propFoo, range=partition) self.foo = EX.foo self.foo.subClassOf = [partitionProp | only | subPartition]
def testValueRestrictionInLeftOfGCI(self): someProp = Property(EX_NS.someProp) leftGCI = (someProp | value | EX.fish) & EX.Bar foo = EX.Foo foo += leftGCI self.assertEqual( repr(leftGCI), 'ex:Bar THAT ( ex:someProp VALUE <http://example.com/fish> )') ruleStore, ruleGraph, network = SetupRuleStore(makeNetwork=True) rules = network.setupDescriptionLogicProgramming( self.ontGraph, addPDSemantics=False, constructNetwork=False) self.assertEqual( repr(rules), "set([Forall ?X ( ex:Foo(?X) :- " + "And( ex:someProp(?X ex:fish) ex:Bar(?X) ) )])")
def testOtherForm2(self): hasCoronaryBypassConduit = Property(EX_NS.hasCoronaryBypassConduit) ITALeft = EX.ITALeft ITALeft += (hasCoronaryBypassConduit | some | EnumeratedClass(members=[ EX_NS.CoronaryBypassConduit_internal_thoracic_artery_left_insitu, EX_NS.CoronaryBypassConduit_internal_thoracic_artery_left_free ])) from FuXi.DLP.DLNormalization import NormalFormReduction self.assertEquals(repr(Class(first(ITALeft.subSumpteeIds()))), "Some Class SubClassOf: Class: ex:ITALeft ") NormalFormReduction(self.ontGraph) self.assertEquals( repr(Class(first(ITALeft.subSumpteeIds()))), 'Some Class SubClassOf: Class: ex:ITALeft . EquivalentTo: ( ( ex:hasCoronaryBypassConduit VALUE <http://example.com/CoronaryBypassConduit_internal_thoracic_artery_left_insitu> ) OR ( ex:hasCoronaryBypassConduit VALUE <http://example.com/CoronaryBypassConduit_internal_thoracic_artery_left_free> ) )' )
def testInConjunct(self): contains = Property(EX_NS.contains) testCase2 = EX.Operation & ~ (contains | some | EX.IsolatedCABGConcomitantExclusion) & \ (contains | some | EX.CoronaryArteryBypassGrafting) testCase2.identifier = EX_NS.IsolatedCABGOperation NormalFormReduction(self.ontGraph) self.assertEqual(repr(testCase2), 'ex:Operation THAT ( ex:contains SOME ex:CoronaryArteryBypassGrafting ) AND ( NOT ( ex:contains SOME ex:IsolatedCABGConcomitantExclusion ) )') ruleStore, ruleGraph, network = SetupRuleStore(makeNetwork=True) op = BNode() (EX.Operation).extent = [op] grafting = BNode() (EX.CoronaryArteryBypassGrafting).extent = [grafting] testCase2.graph.add((op, EX_NS.contains, grafting)) CalculateStratifiedModel( network, testCase2.graph, [EX_NS.Foo, EX_NS.IsolatedCABGOperation]) testCase2.graph = network.inferredFacts self.failUnless(op in testCase2.extent, "%s should be in ex:IsolatedCABGOperation's extent" % op)
def transform(self, graph): """ Transforms a 'pure' nominal range into a disjunction of value restrictions """ Individual.factoryGraph = graph for restriction, intermediateCl, nominal, prop in graph.query( self.NOMINAL_QUERY, initNs={u'owl': OWL_NS}): nominalCollection = Collection(graph, nominal) #purge restriction restr = Class(restriction) parentSets = [i for i in restr.subClassOf] restr.clearOutDegree() newConjunct = BooleanClass( restriction, OWL_NS.unionOf, [Property(prop) | value | val for val in nominalCollection], graph) newConjunct.subClassOf = parentSets #purge nominalization placeholder iClass = BooleanClass(intermediateCl) iClass.clear() iClass.delete()
def main(): from optparse import OptionParser op = OptionParser( 'usage: %prog [options] factFile1 factFile2 ... factFileN') op.add_option( '--why', default=None, help='Specifies the goals to solve for using the non-naive methods' + 'see --method') op.add_option( '--closure', action='store_true', default=False, help='Whether or not to serialize the inferred triples' + ' along with the original triples. Otherwise ' + '(the default behavior), serialize only the inferred triples') op.add_option( '--imports', action='store_true', default=False, help='Whether or not to follow owl:imports in the fact graph') op.add_option( '--output', default='n3', metavar='RDF_FORMAT', choices=[ 'xml', 'TriX', 'n3', 'pml', 'proof-graph', 'nt', 'rif', 'rif-xml', 'conflict', 'man-owl' ], help= "Serialize the inferred triples and/or original RDF triples to STDOUT " + "using the specified RDF syntax ('xml', 'pretty-xml', 'nt', 'turtle', " + "or 'n3') or to print a summary of the conflict set (from the RETE " + "network) if the value of this option is 'conflict'. If the the " + " value is 'rif' or 'rif-xml', Then the rules used for inference " + "will be serialized as RIF. If the value is 'pml' and --why is used, " + " then the PML RDF statements are serialized. If output is " + "'proof-graph then a graphviz .dot file of the proof graph is printed. " + "Finally if the value is 'man-owl', then the RDF facts are assumed " + "to be OWL/RDF and serialized via Manchester OWL syntax. The default is %default" ) op.add_option( '--class', dest='classes', action='append', default=[], metavar='QNAME', help='Used with --output=man-owl to determine which ' + 'classes within the entire OWL/RDF are targetted for serialization' + '. Can be used more than once') op.add_option( '--hybrid', action='store_true', default=False, help='Used with with --method=bfp to determine whether or not to ' + 'peek into the fact graph to identify predicates that are both ' + 'derived and base. This is expensive for large fact graphs' + 'and is explicitely not used against SPARQL endpoints') op.add_option( '--property', action='append', dest='properties', default=[], metavar='QNAME', help='Used with --output=man-owl or --extract to determine which ' + 'properties are serialized / extracted. Can be used more than once') op.add_option( '--normalize', action='store_true', default=False, help= "Used with --output=man-owl to attempt to determine if the ontology is 'normalized' [Rector, A. 2003]" + "The default is %default") op.add_option( '--ddlGraph', default=False, help= "The location of a N3 Data Description document describing the IDB predicates" ) op.add_option( '--input-format', default='xml', dest='inputFormat', metavar='RDF_FORMAT', choices=['xml', 'trix', 'n3', 'nt', 'rdfa'], help= "The format of the RDF document(s) which serve as the initial facts " + " for the RETE network. One of 'xml', 'n3', 'trix', 'nt', " + "or 'rdfa'. The default is %default") op.add_option( '--safety', default='none', metavar='RULE_SAFETY', choices=['loose', 'strict', 'none'], help="Determines how to handle RIF Core safety. A value of 'loose' " + " means that unsafe rules will be ignored. A value of 'strict' " + " will cause a syntax exception upon any unsafe rule. A value of " + "'none' (the default) does nothing") op.add_option( '--pDSemantics', action='store_true', default=False, help= 'Used with --dlp to add pD semantics ruleset for semantics not covered ' + 'by DLP but can be expressed in definite Datalog Logic Programming' + ' The default is %default') op.add_option( '--stdin', action='store_true', default=False, help= 'Parse STDIN as an RDF graph to contribute to the initial facts. The default is %default ' ) op.add_option( '--ns', action='append', default=[], metavar="PREFIX=URI", help='Register a namespace binding (QName prefix to a base URI). This ' + 'can be used more than once') op.add_option( '--rules', default=[], action='append', metavar='PATH_OR_URI', help='The Notation 3 documents to use as rulesets for the RETE network' + '. Can be specified more than once') op.add_option('-d', '--debug', action='store_true', default=True, help='Include debugging output') op.add_option( '--strictness', default='defaultBase', metavar='DDL_STRICTNESS', choices=['loose', 'defaultBase', 'defaultDerived', 'harsh'], help= 'Used with --why to specify whether to: *not* check if predicates are ' + ' both derived and base (loose), if they are, mark as derived (defaultDerived) ' + 'or as base (defaultBase) predicates, else raise an exception (harsh)') op.add_option( '--method', default='naive', metavar='reasoning algorithm', choices=['gms', 'bfp', 'naive'], help='Used with --why to specify how to evaluate answers for query. ' + 'One of: gms, sld, bfp, naive') op.add_option( '--firstAnswer', default=False, action='store_true', help= 'Used with --why to determine whether to fetch all answers or just ' + 'the first') op.add_option( '--edb', default=[], action='append', metavar='EXTENSIONAL_DB_PREDICATE_QNAME', help= 'Used with --why/--strictness=defaultDerived to specify which clashing ' + 'predicate will be designated as a base predicate') op.add_option( '--idb', default=[], action='append', metavar='INTENSIONAL_DB_PREDICATE_QNAME', help= 'Used with --why/--strictness=defaultBase to specify which clashing ' + 'predicate will be designated as a derived predicate') op.add_option( '--hybridPredicate', default=[], action='append', metavar='PREDICATE_QNAME', help= 'Used with --why to explicitely specify a hybrid predicate (in both ' + ' IDB and EDB) ') op.add_option( '--noMagic', default=[], action='append', metavar='DB_PREDICATE_QNAME', help='Used with --why to specify that the predicate shouldnt have its ' + 'magic sets calculated') op.add_option( '--filter', action='append', default=[], metavar='PATH_OR_URI', help= 'The Notation 3 documents to use as a filter (entailments do not particpate in network)' ) op.add_option( '--ruleFacts', action='store_true', default=False, help="Determines whether or not to attempt to parse initial facts from " + "the rule graph. The default is %default") op.add_option( '--builtins', default=False, metavar='PATH_TO_PYTHON_MODULE', help="The path to a python module with function definitions (and a " + "dicitonary called ADDITIONAL_FILTERS) to use for builtins implementations" ) op.add_option( '--dlp', action='store_true', default=False, help= 'Use Description Logic Programming (DLP) to extract rules from OWL/RDF. The default is %default' ) op.add_option( '--sparqlEndpoint', action='store_true', default=False, help= 'Indicates that the sole argument is the URI of a SPARQL endpoint to query' ) op.add_option( '--ontology', action='append', default=[], metavar='PATH_OR_URI', help= 'The path to an OWL RDF/XML graph to use DLP to extract rules from ' + '(other wise, fact graph(s) are used) ') op.add_option( '--ontologyFormat', default='xml', dest='ontologyFormat', metavar='RDF_FORMAT', choices=['xml', 'trix', 'n3', 'nt', 'rdfa'], help= "The format of the OWL RDF/XML graph specified via --ontology. The default is %default" ) op.add_option( '--builtinTemplates', default=None, metavar='N3_DOC_PATH_OR_URI', help= 'The path to an N3 document associating SPARQL FILTER templates to ' + 'rule builtins') op.add_option('--negation', action='store_true', default=False, help='Extract negative rules?') op.add_option( '--normalForm', action='store_true', default=False, help='Whether or not to reduce DL axioms & LP rules to a normal form') (options, facts) = op.parse_args() nsBinds = {'iw': 'http://inferenceweb.stanford.edu/2004/07/iw.owl#'} for nsBind in options.ns: pref, nsUri = nsBind.split('=') nsBinds[pref] = nsUri namespace_manager = NamespaceManager(Graph()) if options.sparqlEndpoint: factGraph = Graph(plugin.get('SPARQLStore', Store)(facts[0])) options.hybrid = False else: factGraph = Graph() ruleSet = Ruleset() for fileN in options.rules: if options.ruleFacts and not options.sparqlEndpoint: factGraph.parse(fileN, format='n3') print("Parsing RDF facts from ", fileN) if options.builtins: import imp userFuncs = imp.load_source('builtins', options.builtins) rs = HornFromN3(fileN, additionalBuiltins=userFuncs.ADDITIONAL_FILTERS) else: rs = HornFromN3(fileN) nsBinds.update(rs.nsMapping) ruleSet.formulae.extend(rs) #ruleGraph.parse(fileN, format='n3') ruleSet.nsMapping = nsBinds for prefix, uri in list(nsBinds.items()): namespace_manager.bind(prefix, uri, override=False) closureDeltaGraph = Graph() closureDeltaGraph.namespace_manager = namespace_manager factGraph.namespace_manager = namespace_manager if not options.sparqlEndpoint: for fileN in facts: factGraph.parse(fileN, format=options.inputFormat) if options.imports: for owlImport in factGraph.objects(predicate=OWL_NS.imports): factGraph.parse(owlImport) print("Parsed Semantic Web Graph.. ", owlImport) if not options.sparqlEndpoint and facts: for pref, uri in factGraph.namespaces(): nsBinds[pref] = uri if options.stdin: assert not options.sparqlEndpoint, "Cannot use --stdin with --sparqlEndpoint" factGraph.parse(sys.stdin, format=options.inputFormat) #Normalize namespace mappings #prune redundant, rdflib-allocated namespace prefix mappings newNsMgr = NamespaceManager(factGraph) from FuXi.Rete.Util import CollapseDictionary for k, v in list( CollapseDictionary( dict([(k, v) for k, v in factGraph.namespaces()])).items()): newNsMgr.bind(k, v) factGraph.namespace_manager = newNsMgr if options.normalForm: NormalFormReduction(factGraph) if not options.sparqlEndpoint: workingMemory = generateTokenSet(factGraph) if options.builtins: import imp userFuncs = imp.load_source('builtins', options.builtins) rule_store, rule_graph, network = SetupRuleStore( makeNetwork=True, additionalBuiltins=userFuncs.ADDITIONAL_FILTERS) else: rule_store, rule_graph, network = SetupRuleStore(makeNetwork=True) network.inferredFacts = closureDeltaGraph network.nsMap = nsBinds if options.dlp: from FuXi.DLP.DLNormalization import NormalFormReduction if options.ontology: ontGraph = Graph() for fileN in options.ontology: ontGraph.parse(fileN, format=options.ontologyFormat) for prefix, uri in ontGraph.namespaces(): nsBinds[prefix] = uri namespace_manager.bind(prefix, uri, override=False) if options.sparqlEndpoint: factGraph.store.bind(prefix, uri) else: ontGraph = factGraph NormalFormReduction(ontGraph) dlp = network.setupDescriptionLogicProgramming( ontGraph, addPDSemantics=options.pDSemantics, constructNetwork=False, ignoreNegativeStratus=options.negation, safety=safetyNameMap[options.safety]) ruleSet.formulae.extend(dlp) if options.output == 'rif' and not options.why: for rule in ruleSet: print(rule) if options.negation: for nRule in network.negRules: print(nRule) elif options.output == 'man-owl': cGraph = network.closureGraph(factGraph, readOnly=False) cGraph.namespace_manager = namespace_manager Individual.factoryGraph = cGraph if options.classes: mapping = dict(namespace_manager.namespaces()) for c in options.classes: pref, uri = c.split(':') print(Class(URIRef(mapping[pref] + uri)).__repr__(True)) elif options.properties: mapping = dict(namespace_manager.namespaces()) for p in options.properties: pref, uri = p.split(':') print(Property(URIRef(mapping[pref] + uri))) else: for p in AllProperties(cGraph): print(p.identifier, first(p.label)) print(repr(p)) for c in AllClasses(cGraph): if options.normalize: if c.isPrimitive(): primAnc = [ sc for sc in c.subClassOf if sc.isPrimitive() ] if len(primAnc) > 1: warnings.warn( "Branches of primitive skeleton taxonomy" + " should form trees: %s has %s primitive parents: %s" % (c.qname, len(primAnc), primAnc), UserWarning, 1) children = [desc for desc in c.subSumpteeIds()] for child in children: for otherChild in [ o for o in children if o is not child ]: if not otherChild in [ c.identifier for c in Class(child).disjointWith ]: # and \ warnings.warn( "Primitive children (of %s) " % (c.qname) + \ "must be mutually disjoint: %s and %s" % ( Class(child).qname, Class(otherChild).qname), UserWarning, 1) # if not isinstance(c.identifier, BNode): print(c.__repr__(True)) if not options.why: # Naive construction of graph for rule in ruleSet: network.buildNetworkFromClause(rule) magicSeeds = [] if options.why: builtinTemplateGraph = Graph() if options.builtinTemplates: builtinTemplateGraph = Graph().parse(options.builtinTemplates, format='n3') factGraph.templateMap = \ dict([(pred, template) for pred, _ignore, template in builtinTemplateGraph.triples( (None, TEMPLATES.filterTemplate, None))]) goals = [] query = ParseSPARQL(options.why) network.nsMap['pml'] = PML network.nsMap['gmp'] = GMP_NS network.nsMap['owl'] = OWL_NS nsBinds.update(network.nsMap) network.nsMap = nsBinds if not query.prologue: query.prologue = Prologue(None, []) query.prologue.prefixBindings.update(nsBinds) else: for prefix, nsInst in list(nsBinds.items()): if prefix not in query.prologue.prefixBindings: query.prologue.prefixBindings[prefix] = nsInst print("query.prologue", query.prologue) print("query.query", query.query) print("query.query.whereClause", query.query.whereClause) print("query.query.whereClause.parsedGraphPattern", query.query.whereClause.parsedGraphPattern) goals.extend([(s, p, o) for s, p, o, c in ReduceGraphPattern( query.query.whereClause.parsedGraphPattern, query.prologue).patterns]) # dPreds=[]# p for s, p, o in goals ] # print("goals", goals) magicRuleNo = 0 bottomUpDerivedPreds = [] # topDownDerivedPreds = [] defaultBasePreds = [] defaultDerivedPreds = set() hybridPredicates = [] mapping = dict(newNsMgr.namespaces()) for edb in options.edb: pref, uri = edb.split(':') defaultBasePreds.append(URIRef(mapping[pref] + uri)) noMagic = [] for pred in options.noMagic: pref, uri = pred.split(':') noMagic.append(URIRef(mapping[pref] + uri)) if options.ddlGraph: ddlGraph = Graph().parse(options.ddlGraph, format='n3') # @TODO: should also get hybrid predicates from DDL graph defaultDerivedPreds = IdentifyDerivedPredicates( ddlGraph, Graph(), ruleSet) else: for idb in options.idb: pref, uri = idb.split(':') defaultDerivedPreds.add(URIRef(mapping[pref] + uri)) defaultDerivedPreds.update( set([p == RDF.type and o or p for s, p, o in goals])) for hybrid in options.hybridPredicate: pref, uri = hybrid.split(':') hybridPredicates.append(URIRef(mapping[pref] + uri)) if options.method == 'gms': for goal in goals: goalSeed = AdornLiteral(goal).makeMagicPred() print("Magic seed fact (used in bottom-up evaluation)", goalSeed) magicSeeds.append(goalSeed.toRDFTuple()) if noMagic: print("Predicates whose magic sets will not be calculated") for p in noMagic: print("\t", factGraph.qname(p)) for rule in MagicSetTransformation( factGraph, ruleSet, goals, derivedPreds=bottomUpDerivedPreds, strictCheck=nameMap[options.strictness], defaultPredicates=(defaultBasePreds, defaultDerivedPreds), noMagic=noMagic): magicRuleNo += 1 network.buildNetworkFromClause(rule) if len(list(ruleSet)): print("reduction in size of program: %s (%s -> %s clauses)" % (100 - (float(magicRuleNo) / float(len(list(ruleSet)))) * 100, len(list(ruleSet)), magicRuleNo)) start = time.time() network.feedFactsToAdd(generateTokenSet(magicSeeds)) if not [ rule for rule in factGraph.adornedProgram if len(rule.sip) ]: warnings.warn( "Using GMS sideways information strategy with no " + "information to pass from query. Falling back to " + "naive method over given facts and rules") network.feedFactsToAdd(workingMemory) sTime = time.time() - start if sTime > 1: sTimeStr = "%s seconds" % sTime else: sTime = sTime * 1000 sTimeStr = "%s milli seconds" % sTime print("Time to calculate closure on working memory: ", sTimeStr) if options.output == 'rif': print("Rules used for bottom-up evaluation") if network.rules: for clause in network.rules: print(clause) else: for clause in factGraph.adornedProgram: print(clause) if options.output == 'conflict': network.reportConflictSet() elif options.method == 'bfp': topDownDPreds = defaultDerivedPreds if options.builtinTemplates: builtinTemplateGraph = Graph().parse(options.builtinTemplates, format='n3') builtinDict = dict([ (pred, template) for pred, _ignore, template in builtinTemplateGraph.triples((None, TEMPLATES.filterTemplate, None)) ]) else: builtinDict = None topDownStore = TopDownSPARQLEntailingStore( factGraph.store, factGraph, idb=ruleSet, DEBUG=options.debug, derivedPredicates=topDownDPreds, templateMap=builtinDict, nsBindings=network.nsMap, identifyHybridPredicates=options.hybrid if options.method == 'bfp' else False, hybridPredicates=hybridPredicates) targetGraph = Graph(topDownStore) for pref, nsUri in list(network.nsMap.items()): targetGraph.bind(pref, nsUri) start = time.time() # queryLiteral = EDBQuery([BuildUnitermFromTuple(goal) for goal in goals], # targetGraph) # query = queryLiteral.asSPARQL() # print("Goal to solve ", query) sTime = time.time() - start result = targetGraph.query(options.why, initNs=network.nsMap) if result.askAnswer: sTime = time.time() - start if sTime > 1: sTimeStr = "%s seconds" % sTime else: sTime = sTime * 1000 sTimeStr = "%s milli seconds" % sTime print("Time to reach answer ground goal answer of %s: %s" % (result.askAnswer[0], sTimeStr)) else: for rt in result: sTime = time.time() - start if sTime > 1: sTimeStr = "%s seconds" % sTime else: sTime = sTime * 1000 sTimeStr = "%s milli seconds" % sTime if options.firstAnswer: break print( "Time to reach answer %s via top-down SPARQL sip strategy: %s" % (rt, sTimeStr)) if options.output == 'conflict' and options.method == 'bfp': for _network, _goal in topDownStore.queryNetworks: print(network, _goal) _network.reportConflictSet(options.debug) for query in topDownStore.edbQueries: print(query.asSPARQL()) elif options.method == 'naive': start = time.time() network.feedFactsToAdd(workingMemory) sTime = time.time() - start if sTime > 1: sTimeStr = "%s seconds" % sTime else: sTime = sTime * 1000 sTimeStr = "%s milli seconds" % sTime print("Time to calculate closure on working memory: ", sTimeStr) print(network) if options.output == 'conflict': network.reportConflictSet() for fileN in options.filter: for rule in HornFromN3(fileN): network.buildFilterNetworkFromClause(rule) if options.negation and network.negRules and options.method in [ 'both', 'bottomUp' ]: now = time.time() rt = network.calculateStratifiedModel(factGraph) print( "Time to calculate stratified, stable model (inferred %s facts): %s" % (rt, time.time() - now)) if options.filter: print("Applying filter to entailed facts") network.inferredFacts = network.filteredFacts if options.closure and options.output in RDF_SERIALIZATION_FORMATS: cGraph = network.closureGraph(factGraph) cGraph.namespace_manager = namespace_manager print( cGraph.serialize(destination=None, format=options.output, base=None)) elif options.output and options.output in RDF_SERIALIZATION_FORMATS: print( network.inferredFacts.serialize(destination=None, format=options.output, base=None))
assert pformat(woman) == '( ex:Female AND ex:Human )' # Enumerated classes can also be manipulated contList = [Class(exNs.Africa, graph=g), Class(exNs.NorthAmerica, graph=g)] assert pformat( EnumeratedClass(members=contList, graph=g)) == \ '{ ex:Africa ex:NorthAmerica }' # owl:Restrictions can also be instanciated: assert pformat(Restriction( exNs.hasParent, graph=g, allValuesFrom=exNs.Human)) == \ '( ex:hasParent ONLY ex:Human )' # Restrictions can also be created using Manchester OWL syntax in # 'colloquial' Python. A Python infix operator recipe was used for # this purpose. See below assert pformat( exNs.hasParent | some | Class(exNs.Physician, graph=g)) == \ '( ex:hasParent SOME ex:Physician )' assert pformat( Property(exNs.hasParent, graph=g) | max | Literal(1)) == \ '( ex:hasParent MAX 1 )' print("Completed")
def ProcessConcept(klass, owlGraph, FreshConcept, newOwlGraph): """ This method implements the pre-processing portion of the completion-based procedure and recursively transforms the input ontology one concept at a time """ iD = klass.identifier # maps the identifier to skolem:bnodeLabel if # the identifier is a BNode or to skolem:newBNodeLabel # if its a URI FreshConcept[iD] = SkolemizeExistentialClasses( BNode() if isinstance(iD, URIRef) else iD ) # A fresh atomic concept (A_c) newCls = Class(FreshConcept[iD], graph=newOwlGraph) cls = CastClass(klass, owlGraph) # determine if the concept is the left, right (or both) # operand of a subsumption axiom in the ontology location = WhichSubsumptionOperand(iD, owlGraph) # log.debug(repr(cls)) if isinstance(iD, URIRef): # An atomic concept? if location in [LEFT_SUBSUMPTION_OPERAND, BOTH_SUBSUMPTION_OPERAND]: log.debug( "Original (atomic) concept appears in the left HS of a subsumption axiom") # If class is left operand of subsumption operator, # assert (in new OWL graph) that A_c subsumes the concept _cls = Class(cls.identifier, graph=newOwlGraph) newCls += _cls log.debug("%s subsumes %s" % (newCls, _cls)) if location in [RIGHT_SUBSUMPTION_OPERAND, BOTH_SUBSUMPTION_OPERAND]: log.debug( "Original (atomic) concept appears in the right HS of a subsumption axiom") # If class is right operand of subsumption operator, # assert that it subsumes A_c _cls = Class(cls.identifier, graph=newOwlGraph) _cls += newCls log.debug("%s subsumes %s" % (_cls, newCls)) elif isinstance(cls, Restriction): if location != NEITHER_SUBSUMPTION_OPERAND: # appears in at least one subsumption operator # An existential role restriction log.debug( "Original (role restriction) appears in a subsumption axiom") role = Property(cls.onProperty, graph=newOwlGraph) fillerCls = ProcessConcept( Class(cls.restrictionRange), owlGraph, FreshConcept, newOwlGraph) # leftCls is (role SOME fillerCls) leftCls = role | some | fillerCls log.debug("let leftCls be %s" % leftCls) if location in [LEFT_SUBSUMPTION_OPERAND, BOTH_SUBSUMPTION_OPERAND]: # if appears as the left operand, we say A_c subsumes # leftCls newCls += leftCls log.debug("%s subsumes leftCls" % newCls) if location in [RIGHT_SUBSUMPTION_OPERAND, BOTH_SUBSUMPTION_OPERAND]: # if appears as right operand, we say left Cls subsumes A_c leftCls += newCls log.debug("leftCls subsumes %s" % newCls) else: assert isinstance(cls, BooleanClass), "Not ELH ontology: %r" % cls assert cls._operator == OWL_NS.intersectionOf, "Not ELH ontology" log.debug( "Original conjunction (or boolean operator wlog ) appears in a subsumption axiom") # A boolean conjunction if location != NEITHER_SUBSUMPTION_OPERAND: members = [ProcessConcept(Class(c), owlGraph, FreshConcept, newOwlGraph) for c in cls] newBoolean = BooleanClass( BNode(), members=members, graph=newOwlGraph) # create a boolean conjunction of the fresh concepts corresponding # to processing each member of the existing conjunction if location in [LEFT_SUBSUMPTION_OPERAND, BOTH_SUBSUMPTION_OPERAND]: # if appears as the left operand, we say the new conjunction # is subsumed by A_c newCls += newBoolean log.debug("%s subsumes %s" % (newCls, newBoolean)) if location in [RIGHT_SUBSUMPTION_OPERAND, BOTH_SUBSUMPTION_OPERAND]: # if appears as the right operand, we say A_c is subsumed by # the new conjunction newBoolean += newCls log.debug("%s subsumes %s" % (newBoolean, newCls)) return newCls