class SubM:
# Initializer
def __init__(self, path_hdt=PATH_LOD, path_eq=PATH_EQ):
self.hdt = HDTDocument(path_hdt)
self.subClassOf = "http://www.w3.org/2000/01/rdf-schema#subClassOf"
self.id_subClassOf = self.hdt.convert_term(
"http://www.w3.org/2000/01/rdf-schema#subClassOf",
IdentifierPosition.Predicate)
self.equivalent = "http://www.w3.org/2002/07/owl#equivalentClass"
self.id_equivalentClass = self.hdt.convert_term(
"http://www.w3.org/2002/07/owl#equivalentClass",
IdentifierPosition.Predicate)
self.graph = nx.DiGraph()
self.equi_graph_manager = None #equiClassManager(path_eq)
self.diagnosed_relations = [] # the result
self.suggestion_on_relations = [
] # from the manual decison and Joe's sameAs data. Triple
self.leaf_classes = set()
# the graph includes all the triples with subClassOf as predicate
def setup_graph(self):
(subclass_triple_ids,
cardinality) = self.enquiry(query=(0, self.id_subClassOf, 0),
mode="default")
collect_pairs = []
for (s_id, _, o_id) in subclass_triple_ids:
# add to the directed graph
collect_pairs.append((s_id, o_id))
self.graph.add_edges_from(collect_pairs)
# for the sake of effeciency, we remove all the leaf nodes of the graph (classes
# that does not have subclasses. They don't participate in any cycle by definition)
def filter_leaf_classes(self):
for c in self.graph.nodes:
#test if this node is a leaf
(_, cardi) = self.enquiry(query=(0, self.id_subClassOf, c),
mode="default")
if cardi == 0:
self.leaf_classes.add(c)
print('there are a total of', len(self.leaf_classes),
'leaf nodes removed')
for c in self.leaf_classes:
self.remove_class(c)
# a similar funtion as that of networkx
def remove_class(self, c, comment='remove'):
if self.graph.has_node(c):
self.graph.remove_node(
c) # this also removes all the edges related
# self.diagnosed_classes[c]= comment
# automatically, remove the related edges connected
# a similar funtion as that of networkx
def remove_class_from(self, cs, comment='remove'):
for c in cs:
self.remove_class(c, comment)
# This is for future use of the SUBMASSIVE system. A user may ignore this for now.
def enquiry(self, query, mode="subm"):
(s, p, o) = query
if mode == "default":
return self.hdt.search_triples_ids(s, p, o)
else:
# examine the filtered part first
pass
# Similar as that of networkx
def remove_relation(self, sub, sup, comment='remove'):
if self.graph.has_edge(sub, sup):
self.graph.remove_edge(sub, sup)
self.diagnose_relations(sub, sup, comment)
# Similar as that of networkx
def remove_relation_from(self, relation_list, comment='remove'):
for (sub, sup) in relation_list:
self.remove_relation(sub, sup, comment)
# there is only one term that has different id when retrieved as Subject or Object
def convert_to_id(self, term):
if term == "akt742:Intangible-Thing":
# this is the only class that has two different ids (as subject and object)
return 2601100675
else:
return self.hdt.convert_term(term, IdentifierPosition.Subject)
# there is only one term that has different id when retrieved as Subject or Object
def convert_to_term(self, id):
if id == 2601100675:
return "akt742:Intangible-Thing"
# this is the only one that has two different ids (as subject and object)
else:
return self.hdt.convert_id(id, IdentifierPosition.Subject)
# remove the reflexive edges
def filter_reflexsive(self):
to_remove = set()
file = open('reflexive.csv', 'w', newline='')
writer = csv.writer(file)
writer.writerow([
"SUBJECT_ID", "SUBJECT", "OBJECT_ID", "OBJECT", "SUGGESTION",
"DECISION"
])
for e in self.graph.edges():
(l, r) = e
if l == r:
to_remove.add(e)
print('Number of removed reflexive relations', len(to_remove))
for (l, r) in to_remove:
l_term = self.convert_to_term(l)
r_term = self.convert_to_term(r)
writer.writerow([l, l_term, r, r_term, 'remove', 'o'])
self.graph.remove_edges_from(list(to_remove))
def print_graph_info(self):
print('there are ', len(self.graph.nodes()), ' nodes')
print('there are ', len(self.graph.edges()), ' edges')
# compare against the owl:sameAs relations and rdfs:equivalentClass relations
# at each iteration, if there is such a edge, then remove this one.
def obtain_unnecessary_relations(self):
to_remove = set()
file = open('equivalent-unnecessary-relations.csv', 'w', newline='')
writer = csv.writer(file)
writer.writerow([
"SUBJECT_ID", "SUBJECT", "OBJECT_ID", "OBJECT", "SUGGESTION",
"DECISION"
])
count_i = 0
count_s = 0
for e in self.graph.edges():
label = ''
(l, r) = e
# convert to terms
l_term = self.convert_to_term(l)
r_term = self.convert_to_term(r)
# Step 1: Equicalence Class
(eq_triple_ids,
cardinality1) = self.enquiry(query=(l, self.id_equivalentClass,
r),
mode="default")
(eq_triple_ids,
cardinality2) = self.enquiry(query=(r, self.id_equivalentClass,
l),
mode="default")
if (cardinality1 == 1 or cardinality2 == 1):
label = 'i'
count_i += 1
# Step 2: owl:sameAs
if (self.equi_graph_manager.test_equivalent(l_term, r_term)):
label += 's'
count_s += 1
if label != '':
to_remove.add(e)
writer.writerow([l, l_term, r, r_term, 'remove', label])
print('count_s = ', count_s)
print('count_i = ', count_i)
print('Number of removed unnecessary relations', len(to_remove))
self.graph.remove_edges_from(list(to_remove))
# for the sake of memory effeciency, we can load these unnecessary relations directly
# this is because the sameas data is very big.
def load_unnecessary_relations(self): # to self.suggestion_on_relations
eq_file = open('equivalent-unnecessary-relations.csv', 'r')
reader = csv.DictReader(eq_file)
for row in reader:
s_id = int(row["SUBJECT_ID"])
o_id = int(row["OBJECT_ID"])
sug = row["SUGGESTION"] # should be remove
self.suggestion_on_relations.append((s_id, o_id, sug))
print(len(self.suggestion_on_relations), ' total relations loaded')
# load the manual decisions on size-two cycles
def load_manually_decided_relations(
self): # to self.suggestion_on_relations
man_file = open('lod-two-cycle.csv', 'r')
reader = csv.DictReader(man_file)
coll_nodes = []
for row in reader:
s_id = int(row["SUBJECT_ID"])
# s = row["SUBJECT"]
o_id = int(row["OBJECT_ID"])
sug = row["SUGGESTION"]
coll_nodes.append(s_id)
coll_nodes.append(o_id)
self.suggestion_on_relations.append((s_id, o_id, sug))
print(len(self.suggestion_on_relations), ' total relations loaded')
return coll_nodes
def find_nodes_in_cycles(self, hint_nodes, max, found_min):
# create a new graph
tmp_graph = self.graph.copy()
# find each node that participate in at least one cycle:
nodes = set()
flag = True # flag for debugging
count_found_cycles = 0
while flag:
try:
c = []
hint_not_working = False #flag
try:
c = nx.find_cycle(tmp_graph,
hint_nodes) # change to simple_cycles ??
except Exception as e:
hint_not_working = True
if hint_not_working:
c = nx.find_cycle(tmp_graph)
count_found_cycles += 1
print('Found Cyclce ', count_found_cycles, ' is: ', c)
c_nodes = [x for (x, y) in c]
(l_tmp, r_tmp) = random.choice(c)
tmp_graph.remove_edge(l_tmp, r_tmp)
nodes.update(c_nodes)
if len(nodes) >= max and count_found_cycles >= found_min:
print('total nodes = ', len(nodes))
flag = False
else:
nodes.update(c_nodes)
hint_nodes = c_nodes + hint_nodes
except Exception as e:
print(e)
# print("There is no cycle anymore")
flag = False
nodes = list(nodes)
print('there are in total ', len(nodes),
' nodes that participate in cycles')
print(nodes)
return nodes
def get_cycles_from_nodes(self, nodes):
coll_cycles = [] # a list, not a set
# obtain a subgraph from the nodes
subg = self.graph.subgraph(nodes)
simp_c = list(nx.simple_cycles(subg))
print(' and these nodes has ', len(simp_c),
' simple cycles among them')
# next, process these cycles and get ready to encode
for c in simp_c:
if len(c) == 2:
(l, r) = c
coll_cycles.append([(l, r), (r, l)])
else:
# print ('original = ', c)
cycle = []
for i in range(len(c)):
j = i + 1
if i == len(c) - 1:
j = 0
cycle.append((c[i], c[j]))
# print ('cycle = ', cycle)
coll_cycles.append(cycle)
return (coll_cycles)
return coll_cycles # get ready for encoding
IdentifierPosition.Predicate)
id_subClassOf = hdt_lod.convert_term(
"http://www.w3.org/2000/01/rdf-schema#subClassOf",
IdentifierPosition.Predicate)
id_equivalentClass = hdt_lod.convert_term(
"http://www.w3.org/2002/07/owl#equivalentClass",
IdentifierPosition.Predicate)
# output some stats of LOD-a-lot
# we can query the HDT file using the term IDs (e.g. rdf:type and equivalentClass) or the URIs (e.g. subClassOf and sameAs)
print("# subjects:", "{:,}".format(hdt_lod.nb_subjects))
print("# predicates:", "{:,}".format(hdt_lod.nb_predicates))
print("# objects:", "{:,}".format(hdt_lod.nb_objects))
(triples, cardinality) = hdt_lod.search_triples("", "", "")
print("# triples:", "{:,}".format(cardinality))
(triples, cardinality) = hdt_lod.search_triples_ids(0, id_type, 0)
print("# rdf:type statements:", "{:,}".format(cardinality))
(triples, cardinality) = hdt_lod.search_triples(
"", "http://www.w3.org/2000/01/rdf-schema#subClassOf", "")
print("# rdfs:subClassOf statements:", "{:,}".format(cardinality))
(triples, cardinality) = hdt_lod.search_triples_ids(0, id_equivalentClass, 0)
print("# owl:equivalentClass statements:", "{:,}".format(cardinality))
(triples,
cardinality) = hdt_lod.search_triples("",
"http://www.w3.org/2002/07/owl#sameAs",
"")
print("# owl:sameAs statements:", "{:,}".format(cardinality))
def serializeObject(obj):
ser_obj = pickle.dumps(obj, protocol=pickle.HIGHEST_PROTOCOL)
class SubP:
# Initializer / Instance Attributes
def __init__(self, path_hdt=PATH_LOD, path_eq=PATH_EQ):
self.hdt = HDTDocument(path_hdt)
self.subClassOf = "http://www.w3.org/2000/01/rdf-schema#subClassOf"
self.id_subClassOf = self.hdt.convert_term(
"http://www.w3.org/2000/01/rdf-schema#subClassOf",
IdentifierPosition.Predicate)
self.equivalent = "http://www.w3.org/2002/07/owl#equivalentClass"
self.id_equivalentClass = self.hdt.convert_term(
"http://www.w3.org/2002/07/owl#equivalentClass",
IdentifierPosition.Predicate)
self.subPropertyOf = "http://www.w3.org/2000/01/rdf-schema#subPropertyOf"
self.id_subPropertyOf = self.hdt.convert_term(
"http://www.w3.org/2000/01/rdf-schema#subPropertyOf",
IdentifierPosition.Predicate)
self.equivalentProperty = "http://www.w3.org/2002/07/owl#equivalentProperty"
self.id_equivalentProperty = self.hdt.convert_term(
"http://www.w3.org/2002/07/owl#equivalentProperty",
IdentifierPosition.Predicate)
self.graph = nx.DiGraph()
self.equi_graph_manager = None #equiClassManager(path_eq)
print('set up the equivalence class manager')
self.diagnosed_relations = [] # the result
self.suggestion_on_relations = [
] # from the manual decison and Joe's sameAs data. Triple
self.leaf_classes = set()
print('finished initialization')
def setup_graph(self):
print('set up the graph')
(subclass_triple_ids,
cardinality) = self.enquiry(query=(0, self.id_subPropertyOf, 0),
mode="default")
collect_pairs = []
for (s_id, _, o_id) in subclass_triple_ids:
# add to the directed graph
collect_pairs.append((s_id, o_id))
print('there are ', len(collect_pairs), 'edges')
self.graph.add_edges_from(collect_pairs)
def convert_to_id(self, term):
if term == "akt742:Intangible-Thing":
# this is the only class that has two different ids (as subject and object)
return 2601100675
else:
return self.hdt.convert_term(term, IdentifierPosition.Subject)
def convert_to_term(self, id):
if id == 2601100675:
return "akt742:Intangible-Thing"
# this is the only one that has two different ids (as subject and object)
else:
return self.hdt.convert_id(id, IdentifierPosition.Subject)
def enquiry(self, query, mode="subp"):
(s, p, o) = query
if mode == "default":
return self.hdt.search_triples_ids(s, p, o)
else:
# examine the filtered part first
pass
def print_info(self, sbj, obj):
predicate_names = [
"http://sw.cyc.com/CycAnnotations_v1#label",
"http://www.w3.org/2000/01/rdf-schema#comment",
"http://www.w3.org/2000/01/rdf-schema#label"
]
s_domain = tldextract.extract(sbj).domain
o_domain = tldextract.extract(obj).domain
# filter that domain
# if (s_domain != DOMAIN and o_domain != DOMAIN):
# # print (DOMAIN)
print('SUBJECT: ', sbj)
for p in predicate_names:
(triples, cardinality) = self.hdt.search_triples(sbj, p, "")
for (s, p, o) in triples:
print('\tPREDICATE: ', p)
print('\t\t Comments/labels :', o, '\n')
print('OBJECT: ', obj)
for p in predicate_names:
(triples, cardinality) = self.hdt.search_triples(obj, p, "")
for (s, p, o) in triples:
print('\tPREDICATE: ', p)
print('\t\t Comments/labels :', o, '\n')
print('\n\n========================\n\n')
def export_cycle(self):
simp_c = list(nx.simple_cycles(self.graph))
print('find simple cycle in graph')
print('there are ', len(simp_c), ' simple cycles')
count1 = 0
count_others = 0
count_sameas = 0
count_eqProp = 0
count_bigger = 0
collect_self_loop = []
collect_eq = []
collect_others = []
collect_bigger = []
for c in simp_c:
if len(c) == 1:
count1 += 1
collect_self_loop.append(c)
elif len(c) == 2:
# print (c)
# for n in c:
# t = self.convert_to_term(n)
# print ('\t', t)
# print ('\n')
l_term = self.convert_to_term(c[0])
r_term = self.convert_to_term(c[1])
# id_equivalentProperty
(subclass_triple_ids, cardinality) = self.enquiry(
query=(c[0], self.id_equivalentProperty, c[1]),
mode="default")
# if (self.equi_graph_manager.test_equivalent(l_term, r_term)):
# print ('There is a owl:sameAs relation in between')
# count_sameas += 1
# collect_eq.append(c)
if (cardinality > 0):
print('There is a owl:equivalentProperty in between')
count_eqProp += 1
collect_eq.append(c)
else:
# self.print_info(c[0], l_term, c[1], r_term)
# print ('a longer one for manual decision:',c )
# count_others += 1
collect_others.append(c)
count_others += 1
else:
count_bigger += 1
collect_bigger.append((c[0], c[1]))
collect_bigger.append((c[1], c[2]))
collect_bigger.append((c[2], c[0]))
print('there are ', count1, ' reflexive cycles')
print('there are ', count_sameas, ' sameAs relations')
print('there are ', count_eqProp, ' eqProp relations')
print('there are ', count_others, ' size-two cycles')
print('there are ', count_bigger, ' bigger cycles')
# export self-loop cycles:
with open(file_name, 'w', newline='') as file:
writer = csv.writer(file)
writer.writerow([
"SUBJECT_ID", "SUBJECT", "OBJECT_ID", "OBJECT", "SUGGESTION",
"DECISION"
])
# write to file
# print ('collect self loop: ',collect_self_loop)
for [s_id] in collect_self_loop:
# convert
s_term = self.convert_to_term(s_id)
o_term = s_term
writer.writerow([s_id, s_term, s_id, o_term, 'remove',
'o']) # removed from automatic method
for (s_id, o_id) in collect_eq:
# convert
s_term = self.convert_to_term(s_id)
o_term = self.convert_to_term(o_id)
writer.writerow([s_id, s_term, o_id, o_term, 'remove',
'e']) # removed from automatic method
for (s_id, o_id) in collect_others:
s_term = self.convert_to_term(s_id)
o_term = self.convert_to_term(o_id)
self.print_info(s_term, o_term)
writer.writerow([s_id, s_term, o_id, o_term, 'remove',
'2']) # removed from manual step
writer.writerow([o_id, o_term, s_id, s_term, 'remove',
'2']) # removed from manual step
for (s_id, o_id) in collect_bigger:
s_term = self.convert_to_term(s_id)
o_term = self.convert_to_term(o_id)
# print ('===a longer cycle ===', c)
writer.writerow([s_id, s_term, o_id, o_term, 'remove',
'x']) # removed from manual step
def load_removed(self):
# 'pre-subP.csv'
subp_file = open('pre-subP.csv', 'r')
reader = csv.DictReader(subp_file)
coll_removed = []
for row in reader:
s_id = int(row["SUBJECT_ID"])
# s = row["SUBJECT"]
o_id = int(row["OBJECT_ID"])
sug = row["SUGGESTION"] # should be remove
if (sug == 'remove'):
coll_removed.append((s_id, o_id))
print('number of removed edges:', len(coll_removed))
self.graph.remove_edges_from(coll_removed)
def test_cycle(self):
try:
c = nx.find_cycle(self.graph) # change to simple_cycles ??
print('cycle = ', c)
except Exception as e:
# hint_not_working = True
print('no cycle')
def export_graph_nt(self, name):
g = Graph()
for (s_id, o_id) in self.graph.edges:
s_term = self.convert_to_term(s_id)
o_term = self.convert_to_term(o_id)
bob = URIRef("http://www.w3.org/2000/01/rdf-schema#subPropertyOf")
g.add((URIRef(s_term), bob, URIRef(o_term)))
# print("--- printing raw triples ---")
# for s, p, o in g:
# print((s, p, o))
g.serialize(destination=name, format='nt')
def generate_reduced():
# Q1 : retrieve the subClassOf relations
# hdt_file = None
# output_filename = None
# output_selfloopClass_filename = None
# output_leafClass_filename = None
# output_intermediateClass_filename = None
# if sys.argv [1] == 'lod':
hdt_file = HDTDocument(PATH_LOD)
# output_filename = 'reduced_lod_subClassOf.csv'
output_selfloopClass_filename = 'lod_reflexive_classes.csv'
output_leafClass_filename = 'lod_leaf_classes.csv'
# output_intermediateClass_filename = 'further_reduced_lod_subClassOf.csv'
output_intermediateClass_filename = 'further_reduced_lod_subClassOf.csv'
# else:
# hdt_file = HDTDocument(PATH_DBpedia)
# output_filename = 'dbpedia_subClassOf.csv'
# output_selfloopClass_filename = 'dbpedia_selfloop_classes.csv'
# output_leafClass_filename = 'dbpedia_leaf_classes.csv'
# output_intermediateClass_filename = 'further_reduced_dbpedia_subClassOf.csv'
subClassOf = "http://www.w3.org/2000/01/rdf-schema#subClassOf"
id_subClassOf = hdt_file.convert_term("http://www.w3.org/2000/01/rdf-schema#subClassOf", IdentifierPosition.Predicate)
count = 0
count_selfloop = 0
count_leaf = 0
count_left = 0
count_output_after_further_reduced = 0 # count left of the further reduced
# removed_leaf_classes = []
(subclass_triples, cardinality) = hdt_file.search_triples("", subClassOf, "")
to_explore_ids = set() # to iterate through
leaf_ids = set()
removed_intermediate_ids = set() # removed intermediate nodes
all_ids = set()
with open(output_filename, 'w', newline='') as file:
writer = csv.writer(file)
writer.writerow([ "SUBJECT_ID", "SUBJECT", "OBJECT_ID", "OBJECT"])
with open(output_intermediateClass_filename, 'w', newline='') as inter_file:
writer_inter = csv.writer(inter_file)
writer_inter.writerow([ "SUBJECT_ID", "SUBJECT", "OBJECT_ID", "OBJECT"])
# Step 1: remove selfloops and leaf nodes
with open(output_selfloopClass_filename, 'w', newline='') as selfloop_file:
writer_selfloop = csv.writer(selfloop_file)
writer_selfloop.writerow([ "ID", "URI"])
with open(output_leafClass_filename, 'w', newline='') as leaf_file:
writer_leaf = csv.writer(leaf_file)
writer_leaf.writerow([ "ID", "URI"])
for (s, p, o) in subclass_triples:
s_id = hdt_file.convert_term(s, IdentifierPosition.Subject)
o_id = hdt_file.convert_term(o, IdentifierPosition.Object)
all_ids.add(s_id)
all_ids.add(o_id)
count += 1
# store it in a csv file
if s == o: # self loop
count_selfloop += 1
writer_selfloop.writerow([s_id, s])
else:
(_, leaf_cardinality) = hdt_file.search_triples("", subClassOf, s)
# test if it is a leaf node
if leaf_cardinality == 0:
# there is no subclass, this is a leaf node/class
# write it to a file and store it
writer_leaf.writerow([s_id, s])
leaf_ids.add(s_id)
count_leaf += 1
# removed_leaf_classes.append(s)
# else:
# # write what's left to the file
# # SKIP: find intermediate for now
# count_left += 1
# writer.writerow([s_id, s, o_id, o])
print ('count leaf statements = ', count_leaf)
print ('count leaf (as set) = ', len (leaf_ids))
print ('count total statements = ', count)
print ('count_total nodes (as set) = ', len (all_ids))
print ('NOW Part 2: Further Reduce ') # further reduce it
visited_sup = set()
# near_leaf_sup = set()
count_one = 0
count_loop = 0
for l_id in leaf_ids:
count_loop += 1
(leaf_triples, cardinality) = hdt_file.search_triples_ids(l_id, id_subClassOf, 0)
# get its superclass id : sup_id
finished_this_leaf = False
if cardinality == 1:
(l_id, lp_id, sup_id) = leaf_triples.next()
(_, sub_cardinality) = hdt_file.search_triples_ids(0, id_subClassOf, sup_id)
if sub_cardinality == 1:
# remove this superclass
count_one += 1
removed_intermediate_ids.add(sup_id)
visited_sup.add(sup_id)
(supsup_triples, cardinality) = hdt_file.search_triples_ids(sup_id, id_subClassOf, 0)
for (sup_id, lp_id, supsup_id) in supsup_triples:
to_explore_ids.add(supsup_id)
finished_this_leaf = True
# normal process
if not finished_this_leaf:
for (l_id, lp_id, sup_id) in leaf_triples:
if (sup_id not in visited_sup):
# lo_id = hdt_file.convert_term(lo, IdentifierPosition.Object)
(sup_triples, cardinality_back) = hdt_file.search_triples_ids(0, id_subClassOf, sup_id)
supflag = True # if this superclass only has leaf nodes
if cardinality_back != 1:
for (child_id, lp_id, sup_id) in sup_triples:
if child_id not in leaf_ids:
sup_flag = False
break
if supflag:
# near_leaf_sup.add(sup_id)
removed_intermediate_ids.add(sup_id)
(supsup_triples, cardinality) = hdt_file.search_triples_ids(sup_id, id_subClassOf, 0)
for (sup_id, lp_id, supsup_id) in supsup_triples:
to_explore_ids.add(supsup_id)
else:
to_explore_ids.add (sup_id)
visited_sup.add (sup_id)
if count_loop %100000 ==0:
print ('leaf nodes processed:', count_loop)
print ('count one = ', count_one)
print ('near-leaf nodes = ', len (removed_intermediate_ids))
print ('total visited nodes = ', len (visited_sup))
print ('non-near-leaf nodes = ', len(visited_sup) - len(removed_intermediate_ids))
print ('to explore = ', len(to_explore_ids))
print ('*********** after this data processing, we have only ', len(to_explore_ids), ' to explore for the next step')
# # finished data- proprocessing,
record_to_explore_size = len (to_explore_ids)
record_iteration = 0
continue_flag = True
while (len(to_explore_ids) != 0 and continue_flag):
# print ('still to explore : ', len(to_explore))
record_iteration +=1
# iternate through this and
n_id = to_explore_ids.pop()
(triples_id, cardinality) = hdt_file.search_triples_ids(0, id_subClassOf, n_id)
flag = True
for (ns_id, np_id, no_id) in triples_id:
# if each ns is either a leaf or intermediate but removed, then we remove it.
# ns_id = hdt_file.convert_term(ns, IdentifierPosition.Object)
if ns_id not in leaf_ids and ns_id not in removed_intermediate_ids:
# Keep it for now
flag = False
break
if flag == True: # we are sure to remove it
removed_intermediate_ids.add (n_id)
else:
to_explore_ids.add (n_id) # add back :(
if record_iteration == 10000:
if record_to_explore_size != len (to_explore_ids):
# print ('leaf nodes visited = ', count_leaf)
print ('total leaf nodes = ', len(leaf_ids))
print ('accummulated removed intermediate = ', len (removed_intermediate_ids))
print ('still to explore = ', len (to_explore_ids))
print ('record to explore = ', record_to_explore_size)
print ('changed = ', record_to_explore_size - len (to_explore_ids))
record_iteration = 0
record_to_explore_size = len (to_explore_ids)
else:
continue_flag = False
# to write down the intermediate removed
print ('*****size of leaf:', len (leaf_ids))
print ('*****size of removed intermediate node :', len (removed_intermediate_ids))
(subclass_triples, cardinality) = hdt_file.search_triples("", subClassOf, "")
for (s,p,o) in subclass_triples:
s_id = hdt_file.convert_term(s, IdentifierPosition.Subject)
o_id = hdt_file.convert_term(o, IdentifierPosition.Object)
# count += 1
# store it in a csv file
if s != o:
# if s is not a leaf node and not a removed intermediate node
if (s_id not in leaf_ids) and (s_id not in removed_intermediate_ids):
# write what's left to the file
count_output_after_further_reduced += 1
# print ('count output after further reduced', count_output_after_further_reduced)
writer_inter.writerow([s_id, s, o_id, o])
# else:
# print ('one of them')
# else:
# print ('nothing')
print ('total entries = ', count)
print ('total self-loops = ', count_selfloop)
print ('total leaf nodes/classes = ', count_leaf)
print ('total left = ', count_left)
print ('perfectage of reduction: ', count_left/count)
print ('=====AFTER FURTHER REDUCTION ======')
print ('There are only ', count_output_after_further_reduced)
print ('perfectage of reduction: ', count_output_after_further_reduced/count)
class SubM:
# Initializer / Instance Attributes
def __init__(self, path_hdt=PATH_LOD):
self.hdt = HDTDocument(path_hdt)
self.subClassOf = "http://www.w3.org/2000/01/rdf-schema#subClassOf"
self.id_subClassOf = self.hdt.convert_term(
"http://www.w3.org/2000/01/rdf-schema#subClassOf",
IdentifierPosition.Predicate)
self.equivalent = "http://www.w3.org/2002/07/owl#equivalentClass"
self.id_equivalentClass = self.hdt.convert_term(
"http://www.w3.org/2002/07/owl#equivalentClass",
IdentifierPosition.Predicate)
self.graph = nx.DiGraph()
self.equi_graph = nx.Graph()
self.diagnosed_relations = {}
self.diagnosed_classes = {}
self.leaf_classes = set()
def setup_graph(self):
(subclass_triple_ids,
cardinality) = self.enquiry(query=(0, self.id_subClassOf, 0),
mode="default")
collect_pairs = []
for (s_id, _, o_id) in subclass_triple_ids:
# add to the directed graph
collect_pairs.append((s_id, o_id))
self.graph.add_edges_from(collect_pairs)
def remove_unnecessary_relations(self):
for n in self.graph.nodes():
# test if there is an edge between this node and another node which is also in the Graph
(eq_triple_ids,
cardinality) = self.enquiry(query=(n, self.id_equivalentClass, 0),
mode="default")
for (_, _, m) in eq_triple_ids:
# test if it is in the Graph
if m in self.graph.nodes():
self.remove_relation(n, m, 'equivalence')
(eq_triple_ids,
cardinality) = self.enquiry(query=(0, self.id_equivalentClass, n),
mode="default")
for (m, _, _) in eq_triple_ids:
# test if it is in the Graph
if m in self.graph.nodes():
self.remove_relation(m, n, 'equivalence')
print('total relations diagnosed:', len(self.diagnosed_relations))
def export_graph(self, export_file=None):
collect_pairs = self.graph.edges
if export_file is not None:
file = open(export_file, 'w', newline='')
writer = csv.writer(file)
writer.writerow(["SUBJECT_ID", "SUBJECT", "OBJECT_ID", "OBJECT"])
for (s_id, o_id) in collect_pairs:
s_term = self.convert_to_term(s_id)
o_term = self.convert_to_term(o_id)
writer.writerow([s_term, s_id, o_term, o_id])
# define a function subgraph, edges
# G.edges
# https://networkx.github.io/documentation/stable/reference/classes/generated/networkx.DiGraph.edges.html#networkx.DiGraph.edges
def remove_all_two_cycles(self):
to_remove = set()
for (l, r) in self.graph.edges():
if (r, l) in self.graph.edges():
to_remove.add((l, r))
to_remove.add((r, l))
print('there are in total', len(to_remove), ' two-cycle edges removed')
for (l, r) in to_remove:
self.graph.remove_edge(l, r)
def load_manual_decisions(self, file, mode="remove"):
#if mode = ignore, then we don't do anything to those marked as unknown
#if mode = remove, then we remove all those that are unknown
l_two = []
two = open(file, newline='')
reader_two = csv.DictReader(two)
for row in reader_two:
if (row['SUBJECT_ID'],
row['OBJECT_ID']) not in self.diagnosed_relations.keys():
if (row['SUGGESTION'] == 'remove'):
self.remove_relation(row['SUBJECT_ID'],
row['OBJECT_ID'],
comment='remove')
else:
self.diagnosed_relations[
row['SUBJECT_ID'],
row['OBJECT_ID']] = row['SUGGESTION']
# l_two.append((row['SUBJECT_ID'], row['OBJECT_ID']))
# if it is labeled as 'remove' then remove,
# if it is labeled as 'unknown' then depends on the mode it is in
print('there are in total ', len(l_two),
' relations removed from mannual decisions')
self.graph.remove_edges_from(l_two)
def enquiry(self, query, mode="subm"):
(s, p, o) = query
if mode == "default":
return self.hdt.search_triples_ids(s, p, o)
else:
# examine the filtered part first
pass
def convert_to_id(self, term):
if term == "akt742:Intangible-Thing":
# this is the only class that has two different ids (as subject and object)
return 2601100675
else:
return self.hdt.convert_term(term, IdentifierPosition.Subject)
def convert_to_term(self, id):
if id == 2601100675:
return "akt742:Intangible-Thing"
# this is the only one that has two different ids (as subject and object)
else:
return self.hdt.convert_id(id, IdentifierPosition.Subject)
def remove_relation(self, sub, sup, comment='remove'):
if self.graph.has_edge(sub, sup):
self.graph.remove_edge(sub, sup)
self.diagnose_relations(sub, sup, comment)
def remove_relation_from(self, relation_list, comment='remove'):
for (sub, sup) in relation_list:
self.remove_relation(sub, sup, comment)
def diagnose_relations(self, sub, sup, comment='default'):
self.diagnosed_relations[(sub, sup)] = comment
# change it to a dictionary?
def diagnose_class(self, c, comment='default'):
self.diagnosed_class[c] = comment
# TODO, split the cases of removal and comment
def remove_class(self, c, comment='remove'):
if self.graph.has_node(c):
self.graph.remove_node(
c) # this also removes all the edges related
self.diagnosed_classes[c] = comment
# TODO, also remove the related edges connected
def remove_class_from(self, cs, comment='remove'):
for c in cs:
self.remove_class(c, comment)
def filter_leaf_classes(self):
count = len(self.diagnosed_classes)
for c in self.graph.nodes:
#test if this node is a leaf
(_, cardi) = self.enquiry(query=(0, self.id_subClassOf, c),
mode="default")
if cardi == 0:
self.leaf_classes.add(c)
for c in self.leaf_classes:
self.remove_class(c)
print('there are a total of',
len(self.diagnosed_classes) - count, 'leaf nodes removed')
def get_domain_from_id(self, id):
t = self.convert_to_term(id)
return tldextract.extract(t).domain
def filter_domain_classes(self, domain):
filtered = set()
for c in self.graph.nodes:
t = self.convert_to_term(c)
if (domain == tldextract.extract(t).domain):
filtered.add(c)
print('a total of ', len(filtered), ' removed w.r.t. domain ', domain)
self.remove_class_from(list(filtered))
def filter_reflexsive(self):
to_remove = set()
for e in self.graph.edges():
(l, r) = e
if l == r:
to_remove.add(e)
print('removed reflexive relations', len(to_remove))
self.graph.remove_edges_from(list(to_remove))
def print_cycles(self):
count = 0
flag = True
while flag:
try:
cycle = nx.find_cycle(self.graph)
print('find cycle', cycle)
(l, r) = cycle[0]
print(self.get_domain_from_id(l))
self.graph.remove_edges_from(cycle)
except Exception as e:
print(e)
flag = False
