def setupDescriptionLogicProgramming(self,
owlN3Graph,
expanded=[],
addPDSemantics=True,
classifyTBox=False,
constructNetwork=True,
derivedPreds=[],
ignoreNegativeStratus=False,
safety=DATALOG_SAFETY_NONE):
rt = [
rule for rule in MapDLPtoNetwork(
self,
owlN3Graph,
complementExpansions=expanded,
constructNetwork=constructNetwork,
derivedPreds=derivedPreds,
ignoreNegativeStratus=ignoreNegativeStratus,
safety=safety)
]
if ignoreNegativeStratus:
rules, negRules = rt
rules = set(rules)
self.negRules = set(negRules)
else:
rules = set(rt)
if constructNetwork:
self.rules.update(rules)
additionalRules = set(AdditionalRules(owlN3Graph))
if addPDSemantics:
from FuXi.Horn.HornRules import HornFromN3
additionalRules.update(HornFromN3(StringIO(non_DHL_OWL_Semantics)))
if constructNetwork:
for rule in additionalRules:
self.buildNetwork(iter(rule.formula.body),
iter(rule.formula.head), rule)
self.rules.add(rule)
else:
rules.update(additionalRules)
if constructNetwork:
rules = self.rules
# noRules = len(rules)
if classifyTBox:
self.feedFactsToAdd(generateTokenSet(owlN3Graph))
# print("##### DLP rules fired against OWL/RDF TBOX", self)
return rules
def CalculateStratifiedModel(network, ontGraph, derivedPreds, edb=None):
posRules, ignored = MapDLPtoNetwork(network,
ontGraph,
constructNetwork=False,
derivedPreds=derivedPreds,
ignoreNegativeStratus=True)
for rule in posRules:
network.buildNetworkFromClause(rule)
network.feedFactsToAdd(generateTokenSet(edb and edb or ontGraph))
for i in ignored:
# Evaluate the Graph pattern, and instanciate the head of the rule with
# the solutions returned
sel, compiler = StratifiedSPARQL(i)
query = compiler.compile(sel)
i.stratifiedQuery = query
vars = sel.projection
for rt in (edb and edb or ontGraph).query(query):
solutions = {}
if isinstance(rt, tuple):
solutions.update(dict(
[(vars[idx], i) for idx, i in enumerate(rt)]))
else:
solutions[vars[0]] = rt
i.solutions = solutions
head = copy.deepcopy(i.formula.head)
head.ground(solutions)
fact = head.toRDFTuple()
network.inferredFacts.add(fact)
network.feedFactsToAdd(generateTokenSet([fact]))
# Now we need to clear assertions that cross the individual,
# concept, relation divide
# toRemove=[]
for s, p, o in network.inferredFacts.triples((None,
RDF.type,
None)):
if s in (edb and edb or ontGraph).predicates() or\
s in [_s for _s, _p, _o in
(edb and edb or ontGraph).triples_choices(
(None,
RDF.type,
[OWL_NS.Class,
OWL_NS.Restriction]))]:
network.inferredFacts.remove((s, p, o))
return posRules, ignored