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 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 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 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 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 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 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 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
