def setUp(self):
self.rule_store, self.rule_graph, self.network = SetupRuleStore(
makeNetwork=True)
self.tBoxGraph = Graph().parse(StringIO(TBOX), format='n3')
self.aBoxGraph = Graph().parse(StringIO(ABOX), format='n3')
NormalFormReduction(self.tBoxGraph)
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 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 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 load_graph(schema, data, additional=None, debug=False):
"""
Load the specified schema, extract rules,
load data and any additional ontologies,
and infer facts.
:param schema: Schema Graph
:type schema: `rdflib.ConjunctiveGraph`
:param components: Component Graph
:type components: `rdflib.ConjunctiveGraph`
:param debug: Whether to print debugging information
:type debug: bool
:returns: (Graph w/ inferred facts, Graph w/o inferred facts)
:rtype: tuple(`rdflib.ConjunctiveGraph`,
`rdflib.ConjunctiveGraph`)
"""
from FuXi.Rete.Util import generateTokenSet
from FuXi.DLP.DLNormalization import NormalFormReduction
from FuXi.Rete.RuleStore import SetupRuleStore
rule_store, rule_graph, network = SetupRuleStore(makeNetwork=True)
additional_schema = ConjunctiveGraph()
len_additional = 0
if additional:
for g in additional:
additional_schema += g
len_additional += len(g)
len_schema = len(schema)
len_data = len(data)
schema += additional_schema
NormalFormReduction(schema)
network.setupDescriptionLogicProgramming(schema)
network.feedFactsToAdd(generateTokenSet(schema))
network.feedFactsToAdd(generateTokenSet(data))
if debug:
print network
print dir(network)
for r in network.rules:
print r
for f in network.inferredFacts:
print f
len_inferred = len(network.inferredFacts)
print "==================="
print "Component Reference"
print "==================="
print "Report Information"
print "=================="
print "Generated by Bobcat @ %s" % datetime.datetime.now()
print ""
print ".. list-table::"
print " :header-rows: 1"
print ""
print rest_list_table_row(["Graph", "Triple Count"])
print rest_list_table_row(["Schema", len_schema])
print rest_list_table_row(["Additional", len_additional])
print rest_list_table_row(["Data", len_data])
print rest_list_table_row(["", ""])
print rest_list_table_row(["Inferred", len_inferred])
print rest_list_table_row(["", ""])
print rest_list_table_row(
["Subtotal", len_schema + len_data + len_inferred + len_additional])
print rest_list_table_row(["", ""])
gall = schema
gall += data
if additional:
for g in additional:
gall += g
gall_inferred = copy.deepcopy(gall)
for f in network.inferredFacts:
gall_inferred.add(f)
print rest_list_table_row(["Union Total", len(gall_inferred)])
return gall_inferred, gall
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 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 and options.naive:
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