def __init__(self, a, cn_path, out_type, out_name, cp_path, start_row):
self.a = a
self.concept_net_start_row = start_row
self.corpus_dict = None
self.concept_net_dict = dict()
self.twf = 0
self.reader_test = ConceptNetReader(cn_path, cp_path)
self.writer_test = ConceptNetWriter(a, out_type, out_name)
# Make a ConceptNet relation to Opencog Link map
"""
Temporary changed due to broken spacetime link types:
"/r/HasSubevent" was "DuringLink",
"/r/FirstSubeventOf" was "StartsLink",
"/r/SubeventOf" was "DuringLink",
"/r/LastSubeventOf" was "EndsLink",
"/r/EffectOf" was "PredictiveImplicationLink",
"/r/Causes" was "PredictiveImplicationLink"
"/r/HasPrerequisite" was "RetroactiveImplicationLink",
"/r/PrerequisiteEventOf" was "RetroactiveImplicationLink",
"""
self.map_dict = \
{
"/r/ConceptuallyRelatedTo": "IntensionalSimilarityLink",
"/r/ThematicKLine": "IntensionalSimilarityLink",
"/r/SuperThematicKLine": "IntensionalInheritanceLink",
"/r/HasProperty": "IntensionalInheritanceLink",
"/r/HasSubevent": "EvaluationLink",
"/r/IsA": "InheritanceLink",
"/r/PropertyOf": "InheritanceLink",
"/r/DefinedAs": "SimilarityLink",
"/r/HasPrerequisite": "EvaluationLink",
"/r/PrerequisiteEventOf": "EvaluationLink",
"/r/FirstSubeventOf": "EvaluationLink",
"/r/SubeventOf": "EvaluationLink",
"/r/LastSubeventOf": "EvaluationLink",
"/r/EffectOf": "EvaluationLink",
"/r/Causes": "EvaluationLink"
}
if types is None:
raise EnvironmentError("Please import AtomSpace lib first.")
def __init__(self, a, cn_path, out_type, out_name, cp_path, start_row):
self.a = a
self.concept_net_start_row = start_row
self.corpus_dict = None
self.concept_net_dict = dict()
self.twf = 0
self.reader_test = ConceptNetReader(cn_path, cp_path)
self.writer_test = ConceptNetWriter(a, out_type, out_name)
# Make a ConceptNet relation to Opencog Link map
"""
Temporary changed due to broken spacetime link types:
"/r/HasSubevent" was "DuringLink",
"/r/FirstSubeventOf" was "StartsLink",
"/r/SubeventOf" was "DuringLink",
"/r/LastSubeventOf" was "EndsLink",
"/r/EffectOf" was "PredictiveImplicationLink",
"/r/Causes" was "PredictiveImplicationLink"
"/r/HasPrerequisite" was "RetroactiveImplicationLink",
"/r/PrerequisiteEventOf" was "RetroactiveImplicationLink",
"""
self.map_dict = \
{
"/r/ConceptuallyRelatedTo": "IntensionalSimilarityLink",
"/r/ThematicKLine": "IntensionalSimilarityLink",
"/r/SuperThematicKLine": "IntensionalInheritanceLink",
"/r/HasProperty": "IntensionalInheritanceLink",
"/r/HasSubevent": "EvaluationLink",
"/r/IsA": "InheritanceLink",
"/r/PropertyOf": "InheritanceLink",
"/r/DefinedAs": "SimilarityLink",
"/r/HasPrerequisite": "EvaluationLink",
"/r/PrerequisiteEventOf": "EvaluationLink",
"/r/FirstSubeventOf": "EvaluationLink",
"/r/SubeventOf": "EvaluationLink",
"/r/LastSubeventOf": "EvaluationLink",
"/r/EffectOf": "EvaluationLink",
"/r/Causes": "EvaluationLink"
}
if types is None:
raise EnvironmentError("Please import AtomSpace lib first.")
class ConceptNetConverter:
def __init__(self, a, cn_path, out_type, out_name, cp_path, start_row):
self.a = a
self.concept_net_start_row = start_row
self.corpus_dict = None
self.concept_net_dict = dict()
self.twf = 0
self.reader_test = ConceptNetReader(cn_path, cp_path)
self.writer_test = ConceptNetWriter(a, out_type, out_name)
# Make a ConceptNet relation to Opencog Link map
"""
Temporary changed due to broken spacetime link types:
"/r/HasSubevent" was "DuringLink",
"/r/FirstSubeventOf" was "StartsLink",
"/r/SubeventOf" was "DuringLink",
"/r/LastSubeventOf" was "EndsLink",
"/r/EffectOf" was "PredictiveImplicationLink",
"/r/Causes" was "PredictiveImplicationLink"
"/r/HasPrerequisite" was "RetroactiveImplicationLink",
"/r/PrerequisiteEventOf" was "RetroactiveImplicationLink",
"""
self.map_dict = \
{
"/r/ConceptuallyRelatedTo": "IntensionalSimilarityLink",
"/r/ThematicKLine": "IntensionalSimilarityLink",
"/r/SuperThematicKLine": "IntensionalInheritanceLink",
"/r/HasProperty": "IntensionalInheritanceLink",
"/r/HasSubevent": "EvaluationLink",
"/r/IsA": "InheritanceLink",
"/r/PropertyOf": "InheritanceLink",
"/r/DefinedAs": "SimilarityLink",
"/r/HasPrerequisite": "EvaluationLink",
"/r/PrerequisiteEventOf": "EvaluationLink",
"/r/FirstSubeventOf": "EvaluationLink",
"/r/SubeventOf": "EvaluationLink",
"/r/LastSubeventOf": "EvaluationLink",
"/r/EffectOf": "EvaluationLink",
"/r/Causes": "EvaluationLink"
}
if types is None:
raise EnvironmentError("Please import AtomSpace lib first.")
# 1. ConceptNet relation to Opencog Link mappings
def to_link_type(self, relation):
return self.map_dict[relation] if relation in self.map_dict \
else "EvaluationLink"
def is_effect_of(self, relation):
return "EvaluationLink" if relation == "/r/EffectOf" else False
# end of 1.
# 2. Calculates the probability and makes TruthValue
def total_freq(self, list_of_word_freq):
# Calculates the term probability for each word by using this formula
# term_probability_of_a_word =
# word_frequency_of_the_word / total_word_frequency_of_all_the_words
total_word_freq = 0
if type(list_of_word_freq) is not list:
return total_word_freq
for i in list_of_word_freq:
total_word_freq += float(i[1])
return total_word_freq
def make_opencog_tv(self, word):
if word in self.concept_net_dict:
return self.concept_net_dict[word]
if (" " + word.upper()) in self.corpus_dict:
mean = float(self.corpus_dict[(" " + word.upper())]) / self.twf
count = .95 # have no reason for this value
self.concept_net_dict[word] = TruthValue(mean, count)
return self.concept_net_dict[word]
else:
mean = 1 / (self.twf + 1)
count = .95 # have no reason for this value
self.concept_net_dict[word] = TruthValue(mean, count)
return self.concept_net_dict[word]
# end of 2.
# 3. Makes OpenCog nodes and links.
def __make_atoms(self, assertion, is_eval_link, link_type=''):
# /c/en/computer -> computer
node_1_name = assertion[1][6:]
node_2_name = assertion[2][6:]
node_1_stv = self.make_opencog_tv(node_1_name)
node_2_stv = self.make_opencog_tv(node_2_name)
link_confidence = float(assertion[4])
if link_confidence > 0.0:
# For true statements we give a strength of 0.5 to 1.0, ramping up
# quickly towards 1.0 as the cn_confidence grows.
confidence_value = 1.0 - math.pow(0.2, link_confidence)/2.0
else:
# For any false statement we currently just set a strength of 0.0,
# basically ignoring the ConceptNet confidence value other than
# to note that it was false.
# TODO: This could probably be improved, but not sure how.
confidence_value = 0.0
# For the STV we use the computed confidence from ConceptNet as the
# 'strength'. Currently just using a 'confidence' of ~0.9 indicating
# that whatever ConceptNet thinks the truth value is, we agree strongly
# with this analysis. Maybe something better could be done, hard to say
# for sure though. The 10000 gets converted to the confidence of ~0.9
# by the constructor.
link_tv = TruthValue(confidence_value, 10000)
node_1 = self.a.add_node(types.ConceptNode, node_1_name, tv=node_1_stv)
node_2 = self.a.add_node(types.ConceptNode, node_2_name, tv=node_2_stv)
if is_eval_link:
relation = assertion[0][3:]
predicate_node = self.a.add_node(
types.PredicateNode,
relation,
tv=link_tv
)
list_link = self.a.add_link(types.ListLink, [node_1, node_2])
eval_link = self.a.add_link(
types.EvaluationLink,
[predicate_node, list_link],
tv=link_tv
)
return eval_link
else:
link = self.a.add_link(
get_type(link_type),
[node_1, node_2],
tv=link_tv
)
return link
def make_eval_link(self, assertion, link_type=''):
return self.__make_atoms(assertion, True, link_type)
def make_direct_link(self, assertion, link_type=''):
return self.__make_atoms(assertion, False, link_type)
# end of 3.
# 4. Main routine
def convert(self):
# lists_of_assertions is a list of list of assertion
lists_of_assertions = \
self.reader_test.read_concept_net_file(self.concept_net_start_row)
# Make a corpus dict
term_lists = self.reader_test.read_corpus_file()
self.corpus_dict = dict(term_lists)
self.twf = self.total_freq(term_lists)
for assertion in lists_of_assertions:
relation = assertion[0]
if self.is_effect_of(relation):
output_atom = self.make_eval_link(assertion)
self.writer_test.write_to_file(output_atom)
if (self.to_link_type(relation) == "EvaluationLink") and \
(self.is_effect_of(relation) != "EvaluationLink"):
# this condition is to prevent repetition of EvaluationLink
output_atom = self.make_eval_link(assertion)
self.writer_test.write_to_file(output_atom)
elif self.to_link_type(relation) != "EvaluationLink":
output_atom = self.make_direct_link(
assertion, self.to_link_type(relation)
)
self.writer_test.write_to_file(output_atom)
class ConceptNetConverter:
def __init__(self, a, cn_path, out_type, out_name, cp_path, start_row):
self.a = a
self.concept_net_start_row = start_row
self.corpus_dict = None
self.concept_net_dict = dict()
self.twf = 0
self.reader_test = ConceptNetReader(cn_path, cp_path)
self.writer_test = ConceptNetWriter(a, out_type, out_name)
# Make a ConceptNet relation to Opencog Link map
"""
Temporary changed due to broken spacetime link types:
"/r/HasSubevent" was "DuringLink",
"/r/FirstSubeventOf" was "StartsLink",
"/r/SubeventOf" was "DuringLink",
"/r/LastSubeventOf" was "EndsLink",
"/r/EffectOf" was "PredictiveImplicationLink",
"/r/Causes" was "PredictiveImplicationLink"
"/r/HasPrerequisite" was "RetroactiveImplicationLink",
"/r/PrerequisiteEventOf" was "RetroactiveImplicationLink",
"""
self.map_dict = \
{
"/r/ConceptuallyRelatedTo": "IntensionalSimilarityLink",
"/r/ThematicKLine": "IntensionalSimilarityLink",
"/r/SuperThematicKLine": "IntensionalInheritanceLink",
"/r/HasProperty": "IntensionalInheritanceLink",
"/r/HasSubevent": "EvaluationLink",
"/r/IsA": "InheritanceLink",
"/r/PropertyOf": "InheritanceLink",
"/r/DefinedAs": "SimilarityLink",
"/r/HasPrerequisite": "EvaluationLink",
"/r/PrerequisiteEventOf": "EvaluationLink",
"/r/FirstSubeventOf": "EvaluationLink",
"/r/SubeventOf": "EvaluationLink",
"/r/LastSubeventOf": "EvaluationLink",
"/r/EffectOf": "EvaluationLink",
"/r/Causes": "EvaluationLink"
}
if types is None:
raise EnvironmentError("Please import AtomSpace lib first.")
# 1. ConceptNet relation to Opencog Link mappings
def to_link_type(self, relation):
return self.map_dict[relation] if relation in self.map_dict \
else "EvaluationLink"
def is_effect_of(self, relation):
return "EvaluationLink" if relation == "/r/EffectOf" else False
# end of 1.
# 2. Calculates the probability and makes TruthValue
def total_freq(self, list_of_word_freq):
# Calculates the term probability for each word by using this formula
# term_probability_of_a_word =
# word_frequency_of_the_word / total_word_frequency_of_all_the_words
total_word_freq = 0
if type(list_of_word_freq) is not list:
return total_word_freq
for i in list_of_word_freq:
total_word_freq += float(i[1])
return total_word_freq
def make_opencog_tv(self, word):
if word in self.concept_net_dict:
return self.concept_net_dict[word]
if (" " + word.upper()) in self.corpus_dict:
mean = float(self.corpus_dict[(" " + word.upper())]) / self.twf
count = .95 # have no reason for this value
self.concept_net_dict[word] = TruthValue(mean, count)
return self.concept_net_dict[word]
else:
mean = 1 / (self.twf + 1)
count = .95 # have no reason for this value
self.concept_net_dict[word] = TruthValue(mean, count)
return self.concept_net_dict[word]
# end of 2.
# 3. Makes OpenCog nodes and links.
def __make_atoms(self, assertion, is_eval_link, link_type=''):
# /c/en/computer -> computer
node_1_name = assertion[1][6:]
node_2_name = assertion[2][6:]
node_1_stv = self.make_opencog_tv(node_1_name)
node_2_stv = self.make_opencog_tv(node_2_name)
link_confidence = float(assertion[4])
if link_confidence > 0.0:
# For true statements we give a strength of 0.5 to 1.0, ramping up
# quickly towards 1.0 as the cn_confidence grows.
confidence_value = 1.0 - math.pow(0.2, link_confidence) / 2.0
else:
# For any false statement we currently just set a strength of 0.0,
# basically ignoring the ConceptNet confidence value other than
# to note that it was false.
# TODO: This could probably be improved, but not sure how.
confidence_value = 0.0
# For the STV we use the computed confidence from ConceptNet as the
# 'strength'. Currently just using a 'confidence' of ~0.9 indicating
# that whatever ConceptNet thinks the truth value is, we agree strongly
# with this analysis. Maybe something better could be done, hard to say
# for sure though. The 10000 gets converted to the confidence of ~0.9
# by the constructor.
link_tv = TruthValue(confidence_value, 10000)
node_1 = self.a.add_node(types.ConceptNode, node_1_name, tv=node_1_stv)
node_2 = self.a.add_node(types.ConceptNode, node_2_name, tv=node_2_stv)
if is_eval_link:
relation = assertion[0][3:]
predicate_node = self.a.add_node(types.PredicateNode,
relation,
tv=link_tv)
list_link = self.a.add_link(types.ListLink, [node_1, node_2])
eval_link = self.a.add_link(types.EvaluationLink,
[predicate_node, list_link],
tv=link_tv)
return eval_link
else:
link = self.a.add_link(get_type(link_type), [node_1, node_2],
tv=link_tv)
return link
def make_eval_link(self, assertion, link_type=''):
return self.__make_atoms(assertion, True, link_type)
def make_direct_link(self, assertion, link_type=''):
return self.__make_atoms(assertion, False, link_type)
# end of 3.
# 4. Main routine
def convert(self):
# lists_of_assertions is a list of list of assertion
lists_of_assertions = \
self.reader_test.read_concept_net_file(self.concept_net_start_row)
# Make a corpus dict
term_lists = self.reader_test.read_corpus_file()
self.corpus_dict = dict(term_lists)
self.twf = self.total_freq(term_lists)
for assertion in lists_of_assertions:
relation = assertion[0]
if self.is_effect_of(relation):
output_atom = self.make_eval_link(assertion)
self.writer_test.write_to_file(output_atom)
if (self.to_link_type(relation) == "EvaluationLink") and \
(self.is_effect_of(relation) != "EvaluationLink"):
# this condition is to prevent repetition of EvaluationLink
output_atom = self.make_eval_link(assertion)
self.writer_test.write_to_file(output_atom)
elif self.to_link_type(relation) != "EvaluationLink":
output_atom = self.make_direct_link(
assertion, self.to_link_type(relation))
self.writer_test.write_to_file(output_atom)
