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)))