heightOcc = M['height_Occ']
g1.add((heightOcc, RDF.type, SBUILDING.Height))
g1.add((heightOcc, RDF.type, PRIVVULN.Metadata))
g1.add((occ, PRIVVULNV2.has, heightOcc))
shoulderCircumferenceOcc = M['shoulderCircumference_Occ']
g1.add((shoulderCircumferenceOcc, RDF.type, SBUILDING.ShoulderCircumference))
g1.add((shoulderCircumferenceOcc, RDF.type, PRIVVULN.Metadata))
g1.add((occ, PRIVVULNV2.has, shoulderCircumferenceOcc))
wightOcc = M['wight_Occ']
g1.add((wightOcc, RDF.type, SBUILDING.Wight))
g1.add((wightOcc, RDF.type, PRIVVULN.Metadata))
g1.add((occ, PRIVVULNV2.has, wightOcc))
genderOcc = M['gender_Occ']
g1.add((genderOcc, RDF.type, SBUILDING.Gender))
g1.add((genderOcc, RDF.type, PRIVVULN.Metadata))
g1.add((occ, PRIVVULNV2.has, genderOcc))
driver = Driver(debug_mode=True)
print("graph has %s statements." % len(g1))
folder = "output/paper/TORS/"
outputName = "10.1145-3408308.3427612"
g1 = driver.run(g1, folder + outputName)
print("graph has %s statements." % len(g1))
g1.serialize(folder+outputName+".rdf")
g1.add((teaching_Room, PRIVVULNV2.has, humidity))
hallway_Room = M['Hallway_Room_sub']
g1.add((hallway_Room, RDF.type, SBUILDING.Hallway_Room))
g1.add((hallway_Room, PRIVVULN.star, occupies[1]))
building = M['Building_sub']
g1.add((building, RDF.type, SBUILDING.Building))
g1.add((building, PRIVVULN.star, teaching_Room))
g1.add((building, PRIVVULN.star, hallway_Room))
contextLocation = M['ContextLocation']
g1.add((contextLocation, RDF.type, SBUILDING.ContextLocation))
g1.add((contextLocation, RDF.type, PRIVVULN.Metadata))
g1.add((building, PRIVVULNV2.has, contextLocation))
outdoorTemperature = M['OutdoorTemperature']
g1.add((outdoorTemperature, RDF.type, SBUILDING.OutdoorTemperature))
g1.add((outdoorTemperature, RDF.type, PRIVVULN.TimeSeries))
g1.add((outdoorTemperature, PRIVVULNV2.TemporalResolution, Literal("300", datatype=XSD.double)))
g1.add((building, PRIVVULNV2.has, outdoorTemperature))
driver = Driver(domain_NS=SBUILDING,debug_mode=True)
folder = "output/Datasets/"
outputName = "Gao et al. 2020"
g1 = driver.run(g1, folder + outputName)
print("graph has %s statements." % len(g1))
g1.serialize(folder+outputName+".rdf")
room = M['Room_GCH']
g1.add((room, RDF.type, SBUILDING.Shared_Office_Room))
g1.add((room, PRIVVULN.star, sensors[0]))
g1.add((room, PRIVVULN.star, sensors[1]))
g1.add((room, PRIVVULN.star, zones[0]))
g1.add((room, PRIVVULN.star, zones[1]))
g1.add((room, PRIVVULN.star, zones[2]))
floor = M['Floor_GCH']
g1.add((floor, RDF.type, SBUILDING.Floor))
g1.add((floor, PRIVVULN.star, room))
g1.add((floor, PRIVVULN.star, sensors[2]))
g1.add((floor, PRIVVULN.star, sensors[3]))
building = M['Building_GCH']
g1.add((building, RDF.type, SBUILDING.Building))
g1.add((building, PRIVVULN.star, floor))
driver = Driver()
print("graph has %s statements." % len(g1))
folder = "output/Datasets/"
outputName = "Office_building_2"
g1 = driver.run(g1, folder + outputName)
print("graph has %s statements." % len(g1))
g1.serialize(folder + outputName + ".rdf")
def run_analyses(json):
g1 = Graph()
# source namespaces
RDF = NSUtil.get_namespase_rdf()
RDFS = NSUtil.get_namespase_rdfs()
OWL = NSUtil.get_namespase_owl()
XSD = NSUtil.get_namespase_xsd()
PRIVVULN = NSUtil.get_namespase_base_ontology()
PRIVVULNV2 = NSUtil.get_namespase_extrantion_ontology()
SBUILDING = NSUtil.get_namespase_domain_smart_building()
g1.bind('rdf', RDF)
g1.bind('rdfs', RDFS)
g1.bind('owl', OWL)
g1.bind('xsd', XSD)
# custom namespace
g1.bind('privvuln', PRIVVULN)
g1.bind('privvulnv2', PRIVVULNV2)
g1.bind('sbuilding', SBUILDING)
nodes = json["nodes"]
links = json["links"]
namespace = json["namespace"]
M = Namespace(namespace)
g1.bind('m', M)
for node in nodes:
if "name" not in node and "type" not in node:
continue
subject = M[node["name"]]
g1.add((subject, RDF.type, rdflib.term.URIRef(node["type"])))
if "superType" in node and node["superType"] != "" and node[
"superType"] and node["superType"] != str(PRIVVULNV2.Context):
g1.add((subject, RDF.type, rdflib.term.URIRef(node["superType"])))
if "attributes" in node:
for attribute in node["attributes"]:
if "name" not in attribute and "value" not in attribute and "dataType" not in attribute:
continue
if attribute["dataType"] == "int" or attribute[
"dataType"] == "double" or attribute[
"dataType"] == "string":
if attribute["value"] != '' and attribute["value"] != None:
g1.add(
(subject,
rdflib.term.URIRef(
str(PRIVVULNV2) + attribute["name"]),
Literal(attribute["value"],
datatype=rdflib.term.URIRef(
str(XSD) + attribute["dataType"]))))
for link in links:
if "subject" not in link and "predicate" not in link and "object" not in link:
continue
g1.add((rdflib.term.URIRef(link["subject"]),
rdflib.term.URIRef(link["predicate"]),
rdflib.term.URIRef(link["object"])))
driver = Driver(debug_mode=True)
print("graph has %s statements." % len(g1))
g1, risk_results, graph = driver.run_with_output(g1)
print("graph has %s statements." % len(g1))
returnJson = {
'graph': str(graph.source),
'privacy_report': risk_results,
}
return returnJson
