def reason_func(resource_name):
famNs = Namespace('file:///code/ganglia/metric.n3#')
nsMapping = {'mtc': famNs}
rules = HornFromN3('ganglia/metric/metric_rule.n3')
factGraph = Graph().parse('ganglia/metric/metric.n3', format='n3')
factGraph.bind('mtc', famNs)
dPreds = [famNs.relateTo]
topDownStore = TopDownSPARQLEntailingStore(factGraph.store,
factGraph,
idb=rules,
derivedPredicates=dPreds,
nsBindings=nsMapping)
targetGraph = Graph(topDownStore)
targetGraph.bind('ex', famNs)
#get list of the related resource
r_list = list(
targetGraph.query('SELECT ?RELATETO { mtc:%s mtc:relateTo ?RELATETO}' %
resource_name,
initNs=nsMapping))
res_list = []
for res in r_list:
res_list.append(str(res).split("#")[1])
return res_list
def testQueryMemoization(self):
raise SkipTest(
"SKIPFAIL testQueryMemoization, see test/testBFPQueryMemoization.py"
)
topDownStore = TopDownSPARQLEntailingStore(
self.owlGraph.store,
self.owlGraph,
idb=self.program,
DEBUG=False,
nsBindings=nsMap,
decisionProcedure=BFP_METHOD,
identifyHybridPredicates=True)
targetGraph = Graph(topDownStore)
for pref, nsUri in nsMap.items():
targetGraph.bind(pref, nsUri)
goal = (Variable('SUBJECT'), RDF.type, EX.C)
queryLiteral = EDBQuery([BuildUnitermFromTuple(goal)], self.owlGraph,
[Variable('SUBJECT')])
query = queryLiteral.asSPARQL()
# rt=targetGraph.query(query,initNs=nsMap)
# if len(topDownStore.edbQueries) == len(set(topDownStore.edbQueries)):
# pprint(topDownStore.edbQueries)
print("Queries dispatched against EDB")
for query in self.owlGraph.queriesDispatched:
print(query)
self.failUnlessEqual(len(self.owlGraph.queriesDispatched), 4,
"Duplicate query")
def testTransitivity(self):
nsBindings={u'owl':OWL_NS,u'ex':EX}
topDownStore=TopDownSPARQLEntailingStore(
self.graph.store,
self.graph,
idb=HornFromN3(StringIO(RULES)),
DEBUG=True,
derivedPredicates = [OWL_NS.sameAs],
nsBindings=nsBindings,
hybridPredicates = [OWL_NS.sameAs])
targetGraph = Graph(topDownStore)
for query,solns in QUERIES.items():
result = set(targetGraph.query(query,initNs=nsBindings))
print query,result
self.failUnless(not solns.difference(result))
def setUp(self):
self.famNs = Namespace('http://dev.w3.org/2000/10/swap/test/cwm/fam.n3#')
self.nsMapping = dict(fam=self.famNs)
# self.rules = HornFromN3('http://dev.w3.org/2000/10/swap/test/cwm/fam-rules.n3')
self.rules = HornFromN3(StringIO(rules))
# self.factGraph = Graph().parse(
# 'http://dev.w3.org/2000/10/swap/test/cwm/fam.n3', format='n3')
self.factGraph = Graph().parse(StringIO(facts), format='n3')
self.factGraph.bind('fam', self.famNs)
self.factGraph.bind('', self.famNs)
dPreds = [self.famNs.ancestor]
self.topDownStore = TopDownSPARQLEntailingStore(
self.factGraph.store,
self.factGraph,
idb=self.rules,
derivedPredicates=dPreds,
nsBindings=self.nsMapping)
def WhichSubsumptionOperand(term, owlGraph):
topDownStore = TopDownSPARQLEntailingStore(
owlGraph.store,
owlGraph,
idb=HornFromN3(StringIO(SUBSUMPTION_SEMANTICS)),
DEBUG=False,
derivedPredicates=[OWL_NS.sameAs],
hybridPredicates=[OWL_NS.sameAs])
targetGraph = Graph(topDownStore)
appearsLeft = targetGraph.query("ASK { <%s> rdfs:subClassOf [] } ",
initNs={u'rdfs': RDFS})
appearsRight = targetGraph.query("ASK { [] rdfs:subClassOf <%s> } ",
initNs={u'rdfs': RDFS})
if appearsLeft and appearsRight:
return BOTH_SUBSUMPTION_OPERAND
elif appearsLeft:
return LEFT_SUBSUMPTION_OPERAND
else:
return RIGHT_SUBSUMPTION_OPERAND
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))
def main():
g = Graph()
Individual.factoryGraph = g
g.bind('ex', EX_NS, override=False)
isChildOf = Property(EX_NS.isChildOf)
isMarriedTo = Property(EX_NS.isMarriedTo)
woman = Class(EX_NS.Woman)
man = Class(EX_NS.Man,
subClassOf=[isMarriedTo|only|woman],
# complementOf=woman
)
woman.subClassOf = [isMarriedTo|only|man]
# Class(OWL_NS.Thing,subClassOf=[isMarriedTo|min|Literal(1)])
man.extent = [EX_NS.John,EX_NS.Tim]
woman.extent = [EX_NS.Kate,EX_NS.Mary]
#Semantically equivalent to Abox assertion below
# anon_cls1 = Class(
# subClassOf=[isMarriedTo|some|EnumeratedClass(members=[EX_NS.Mary])]
# )
# anon_cls1.extent = [EX_NS.John]
g.add((EX_NS.John,isMarriedTo.identifier,EX_NS.Mary))
#Semantically equivalent to Abox assertion below
# anon_cls2 = Class(
# subClassOf=[isChildOf|some|EnumeratedClass(members=[EX_NS.John])]
# )
# anon_cls2.extent = [EX_NS.Kate]
g.add((EX_NS.Kate,isChildOf.identifier,EX_NS.John))
#Semantically equivalent to Abox assertion below
# anon_cls3 = Class(
# subClassOf=[isChildOf|some|EnumeratedClass(members=[EX_NS.Mary])]
# )
# anon_cls3.extent = [EX_NS.Tim]
g.add((EX_NS.Tim,isChildOf.identifier,EX_NS.Mary))
print g.serialize(format='pretty-xml')
rule_store, rule_graph, network = SetupRuleStore(makeNetwork=True)
network.nsMap = { u'ex' : EX_NS }
# NormalFormReduction(g)
dlp=network.setupDescriptionLogicProgramming(
g,
addPDSemantics=False,
constructNetwork=False
)
for rule in dlp:
print rule
topDownStore=TopDownSPARQLEntailingStore(
g.store,
g,
idb=dlp,
DEBUG=True,
derivedPredicates=[EX_NS.Man,EX_NS.Woman],
nsBindings=network.nsMap,
identifyHybridPredicates = True)
targetGraph = Graph(topDownStore)
rt=targetGraph.query("ASK { ex:Tim ex:isMarriedTo ex:John }",
initNs=network.nsMap)
print rt.askAnswer[0]
topDownStore.DEBUG = False
for ind in g.query("SELECT ?ind { ?ind a ?class FILTER(isUri(?ind) && ?class != owl:Class ) }"):
print "Individual: ", ind
print "--- Children ---"
for child in targetGraph.query("SELECT ?child { ?child ex:isChildOf %s }"%ind.n3(),
initNs=network.nsMap):
print "\t- ", child
print "----------------"
def MagicOWLProof(self, goals, rules, factGraph, conclusionFile):
progLen = len(rules)
magicRuleNo = 0
dPreds = []
for rule in AdditionalRules(factGraph):
rules.append(rule)
if not GROUND_QUERY and REASONING_STRATEGY != 'gms':
goalDict = dict([((Variable('SUBJECT'), goalP, goalO), goalS)
for goalS, goalP, goalO in goals])
goals = goalDict.keys()
assert goals
if REASONING_STRATEGY == 'gms':
for rule in MagicSetTransformation(factGraph,
rules,
goals,
dPreds):
magicRuleNo += 1
self.network.buildNetworkFromClause(rule)
self.network.rules.add(rule)
if DEBUG:
log.debug("\t", rule)
log.debug("rate of reduction in the size of the program: ",
(100 - (float(magicRuleNo) / float(progLen)) * 100))
if REASONING_STRATEGY in ['bfp', 'sld']: # and not GROUND_QUERY:
reasoningAlg = TOP_DOWN_METHOD if REASONING_STRATEGY == 'sld' \
else BFP_METHOD
topDownStore = TopDownSPARQLEntailingStore(
factGraph.store,
factGraph,
idb=rules,
DEBUG=DEBUG,
nsBindings=nsMap,
decisionProcedure=reasoningAlg,
identifyHybridPredicates=REASONING_STRATEGY == 'bfp')
targetGraph = Graph(topDownStore)
for pref, nsUri in nsMap.items():
targetGraph.bind(pref, nsUri)
start = time.time()
for goal in goals:
queryLiteral = EDBQuery([BuildUnitermFromTuple(goal)],
factGraph,
None if GROUND_QUERY else [goal[0]])
query = queryLiteral.asSPARQL()
log.debug("Goal to solve ", query)
rt = targetGraph.query(query, initNs=nsMap)
if GROUND_QUERY:
self.failUnless(rt.askAnswer[0], "Failed top-down problem")
else:
if (goalDict[goal]) not in rt or DEBUG:
for network, _goal in topDownStore.queryNetworks:
log.debug(network, _goal)
network.reportConflictSet(True)
for query in topDownStore.edbQueries:
log.debug(query.asSPARQL())
self.failUnless((goalDict[goal]) in rt,
"Failed top-down problem")
sTime = time.time() - start
if sTime > 1:
sTimeStr = "%s seconds" % sTime
else:
sTime = sTime * 1000
sTimeStr = "%s ms" % sTime
return sTimeStr
elif REASONING_STRATEGY == 'gms':
for goal in goals:
adornedGoalSeed = AdornLiteral(goal).makeMagicPred()
goal = adornedGoalSeed.toRDFTuple()
if DEBUG:
log.debug("Magic seed fact ", adornedGoalSeed)
factGraph.add(goal)
timing = self.calculateEntailments(factGraph)
for goal in goals:
# self.failUnless(goal in self.network.inferredFacts or goal in factGraph,
# "Failed GMS query")
if goal not in self.network.inferredFacts and goal not in factGraph:
log.debug("missing triple %s" % (pformat(goal)))
# print(list(factGraph.adornedProgram))
# from FuXi.Rete.Util import renderNetwork
# dot = renderNetwork(
# self.network,self.network.nsMap).write_jpeg('test-fail.jpeg')
self.network.reportConflictSet(True)
log.debug("=== Failed: %s ====" % pformat(goal))
else:
log.debug("=== Passed! ===")
return timing
def main():
g = Graph()
Individual.factoryGraph = g
g.bind('ex', EX_NS, override=False)
isChildOf = Property(EX_NS.isChildOf)
isMarriedTo = Property(EX_NS.isMarriedTo)
woman = Class(EX_NS.Woman)
man = Class(
EX_NS.Man,
subClassOf=[isMarriedTo | only | woman],
# complementOf=woman
)
woman.subClassOf = [isMarriedTo | only | man]
# Class(OWL_NS.Thing,subClassOf=[isMarriedTo|min|Literal(1)])
man.extent = [EX_NS.John, EX_NS.Tim]
woman.extent = [EX_NS.Kate, EX_NS.Mary]
#Semantically equivalent to Abox assertion below
# anon_cls1 = Class(
# subClassOf=[isMarriedTo|some|EnumeratedClass(members=[EX_NS.Mary])]
# )
# anon_cls1.extent = [EX_NS.John]
g.add((EX_NS.John, isMarriedTo.identifier, EX_NS.Mary))
#Semantically equivalent to Abox assertion below
# anon_cls2 = Class(
# subClassOf=[isChildOf|some|EnumeratedClass(members=[EX_NS.John])]
# )
# anon_cls2.extent = [EX_NS.Kate]
g.add((EX_NS.Kate, isChildOf.identifier, EX_NS.John))
#Semantically equivalent to Abox assertion below
# anon_cls3 = Class(
# subClassOf=[isChildOf|some|EnumeratedClass(members=[EX_NS.Mary])]
# )
# anon_cls3.extent = [EX_NS.Tim]
g.add((EX_NS.Tim, isChildOf.identifier, EX_NS.Mary))
print g.serialize(format='pretty-xml')
rule_store, rule_graph, network = SetupRuleStore(makeNetwork=True)
network.nsMap = {u'ex': EX_NS}
# NormalFormReduction(g)
dlp = network.setupDescriptionLogicProgramming(g,
addPDSemantics=False,
constructNetwork=False)
for rule in dlp:
print rule
topDownStore = TopDownSPARQLEntailingStore(
g.store,
g,
idb=dlp,
DEBUG=True,
derivedPredicates=[EX_NS.Man, EX_NS.Woman],
nsBindings=network.nsMap,
identifyHybridPredicates=True)
targetGraph = Graph(topDownStore)
rt = targetGraph.query("ASK { ex:Tim ex:isMarriedTo ex:John }",
initNs=network.nsMap)
print rt.askAnswer[0]
topDownStore.DEBUG = False
for ind in g.query(
"SELECT ?ind { ?ind a ?class FILTER(isUri(?ind) && ?class != owl:Class ) }"
):
print "Individual: ", ind
print "--- Children ---"
for child in targetGraph.query(
"SELECT ?child { ?child ex:isChildOf %s }" % ind.n3(),
initNs=network.nsMap):
print "\t- ", child
print "----------------"
from FuXi.SPARQL.BackwardChainingStore import TopDownSPARQLEntailingStore
from FuXi.Horn.HornRules import HornFromN3
from rdflib.graph import Graph
from rdflib import Namespace
from pprint import pprint
famNs = Namespace('http://dev.w3.org/2000/10/swap/test/cwm/fam.n3#')
nsMapping = {u'fam': famNs}
rules = HornFromN3('http://dev.w3.org/2000/10/swap/test/cwm/fam-rules.n3')
factGraph = Graph().parse('http://dev.w3.org/2000/10/swap/test/cwm/fam.n3',
format='n3')
factGraph.bind(u'fam', famNs)
print(factGraph.serialize(format="n3"))
dPreds = [famNs.ancestor]
topDownStore = TopDownSPARQLEntailingStore(factGraph.store,
factGraph,
idb=rules,
derivedPredicates=dPreds,
nsBindings=nsMapping)
targetGraph = Graph(topDownStore)
targetGraph.bind(u'ex', famNs)
pprint(
list(
targetGraph.query(
'SELECT ?ANCESTOR { fam:david fam:ancestor ?ANCESTOR }',
initNs=nsMapping)))