def choose_small_vocabulary(big_frame, concepts_filename, language):
"""
Choose the vocabulary of the small frame, by eliminating the terms which:
- contain more than one word
- are not in ConceptNet
- are not frequent
"""
concepts = set(line.strip() for line in open(concepts_filename))
vocab = []
for term in big_frame.index:
if '_' not in term and term in concepts:
try:
frequency = word_frequency(uri_to_label(term),
language,
wordlist='large')
except LookupError:
frequency = word_frequency(uri_to_label(term),
language,
wordlist='combined')
vocab.append((term, frequency))
small_vocab = [
term for term, frequency in sorted(
vocab, key=lambda x: x[1], reverse=True)[:50000]
]
return small_vocab
def _get_concepts(uri_word, conceptnet_dict=None):
#What about duplicates ??-Don't care, just keep track.
associated_nodes = []
normalized_word = uri_to_label(uri_word)
normalized_word = uri_to_label(uri_word)
associated_nodes.append(normalized_word)
if conceptnet_dict == None:
try:
obj = requests.get('{}{}'.format("http://api.conceptnet.io",
uri_word)).json()
edges = obj["edges"]
except Exception:
print("Request error")
edges = []
else:
if normalized_word not in conceptnet_dict:
try:
obj = requests.get('{}{}'.format("http://api.conceptnet.io",
uri_word)).json()
edges = obj["edges"]
conceptnet_dict[normalized_word] = edges
except Exception:
print("Request error")
edges = []
else:
edges = conceptnet_dict[normalized_word]
at_least_one_edge = False
for edge in edges:
start_node = edge["start"]["term"]
end_node = edge["end"]["term"]
rel = edge["rel"]["label"]
normalized_start_word = uri_to_label(
start_node) # uri_to_label("/c/en/movies") -> "movies"
normalized_end_word = uri_to_label(end_node)
if (rel in DISCARDED_RELATIONS or rel not in RELATIONS
or edge["start"]["language"] != "en"
or edge["end"]["language"] != "en"
or (normalized_start_word in associated_nodes
and normalized_end_word in associated_nodes)):
continue
#total_vocab_list-->associated_nodes
at_least_one_edge = True
if normalized_start_word not in associated_nodes:
associated_nodes.append(normalized_start_word)
#total_vocab_list-->associated_node
if normalized_end_word not in associated_nodes:
associated_nodes.append(normalized_end_word)
#total_vocab_list-->associated_node
return associated_nodes
def ld_node(uri, label=None):
"""
Convert a ConceptNet URI into a dictionary suitable for Linked Data.
"""
if label is None:
label = uri_to_label(uri)
ld = {'@id': uri, 'label': label}
if is_term(uri):
pieces = split_uri(uri)
ld['language'] = get_uri_language(uri)
if len(pieces) > 3:
ld['sense_label'] = '/'.join(pieces[3:])
ld['term'] = uri_prefix(uri)
ld['@type'] = 'Node'
elif uri.startswith('http'):
domain = urlparse(uri).netloc
ld['site'] = domain
ld['term'] = uri
# OpenCyc is down and UMBEL doesn't host their vocabulary on the
# Web. This property indicates whether you can follow a link
# via HTTP and retrieve more information.
ld['site_available'] = domain not in {'sw.opencyc.org', 'umbel.org'}
ld['@type'] = 'Node'
elif uri.startswith('/r/'):
ld['@type'] = 'Relation'
return ld
def ld_node(uri, label=None):
"""
Convert a ConceptNet URI into a dictionary suitable for Linked Data.
"""
if label is None:
label = uri_to_label(uri)
ld = {
'@id': uri,
'label': label
}
if is_term(uri):
pieces = split_uri(uri)
ld['language'] = get_uri_language(uri)
if len(pieces) > 3:
ld['sense_label'] = '/'.join(pieces[3:])
ld['term'] = uri_prefix(uri)
elif uri.startswith('http'):
domain = urlparse(uri).netloc
ld['site'] = domain
ld['term'] = uri
return ld
def get_vector(frame, label, language=None):
"""
Returns the row of a vector-space DataFrame `frame` corresponding
to the text `text`. If `language` is set, this can take in plain text
and normalize it to ConceptNet form. Either way, it can also take in
a label that is already in ConceptNet form.
"""
if frame.index[1].startswith('/c/'): # This frame has URIs in its index
if not label.startswith('/'):
label = standardized_uri(language, label)
try:
return frame.loc[label]
except KeyError:
return pd.Series(index=frame.columns)
else:
if label.startswith('/'):
label = uri_to_label(label)
try:
return frame.loc[replace_numbers(label)]
except KeyError:
# Return a vector of all NaNs
return pd.Series(index=frame.columns)
def ld_node(uri, label=None):
"""
Convert a ConceptNet URI into a dictionary suitable for Linked Data.
"""
if label is None:
label = uri_to_label(uri)
ld = {'@id': uri, 'label': label}
if is_term(uri):
pieces = split_uri(uri)
ld['language'] = get_uri_language(uri)
# Get a reasonably-distinct sense label for the term.
# Usually it will be the part of speech, but when we have fine-grained
# information from Wikipedia or WordNet, it'll include the last
# component as well.
if len(pieces) > 3:
ld['sense_label'] = pieces[3]
if len(pieces) > 4 and pieces[4] in ('wp', 'wn'):
ld['sense_label'] += ', ' + pieces[-1]
ld['term'] = uri_prefix(uri)
ld['@type'] = 'Node'
elif uri.startswith('http'):
domain = urlparse(uri).netloc
ld['site'] = domain
ld['term'] = uri
# OpenCyc is down and UMBEL doesn't host their vocabulary on the
# Web. This property indicates whether you can follow a link
# via HTTP and retrieve more information.
ld['site_available'] = True
if domain in {'sw.opencyc.org', 'umbel.org', 'wikidata.dbpedia.org'}:
ld['site_available'] = False
ld['path'] = urlparse(uri).path
ld['@type'] = 'Node'
elif uri.startswith('/r/'):
ld['@type'] = 'Relation'
return ld
def ld_node(uri, label=None):
"""
Convert a ConceptNet URI into a dictionary suitable for Linked Data.
"""
if label is None:
label = uri_to_label(uri)
ld = {'@id': uri, 'label': label}
if is_term(uri):
pieces = split_uri(uri)
ld['language'] = get_uri_language(uri)
# Get a reasonably-distinct sense label for the term.
# Usually it will be the part of speech, but when we have fine-grained
# information from Wikipedia or WordNet, it'll include the last
# component as well.
if len(pieces) > 3:
ld['sense_label'] = pieces[3]
if len(pieces) > 4 and pieces[4] in ('wp', 'wn'):
ld['sense_label'] += ', ' + pieces[-1]
ld['term'] = uri_prefix(uri)
ld['@type'] = 'Node'
elif uri.startswith('http'):
domain = urlparse(uri).netloc
ld['site'] = domain
ld['term'] = uri
# OpenCyc is down and UMBEL doesn't host their vocabulary on the
# Web. This property indicates whether you can follow a link
# via HTTP and retrieve more information.
ld['site_available'] = domain not in {'sw.opencyc.org', 'umbel.org'}
ld['@type'] = 'Node'
elif uri.startswith('/r/'):
ld['@type'] = 'Relation'
return ld
def generate_graph(toks,
postags,
nertags,
conceptnet_dict=None,
verbose=False):
SEED = 1234
random.seed(SEED)
primary_vocab_list = []
total_vocab_list = []
edges_list = []
concepts = []
toks_len = len(toks)
i = 0
while i < toks_len:
# Adds entities (n_grams)
if nertags[i] in ENTITY_BEGINNING:
ngram_list = [toks[i].text]
j = i + 1
while j < toks_len and nertags[j] in ENTITY_INSIDE:
ngram_list.append(toks[j].text)
j += 1
ngram = ' '.join(ngram_list)
concepts.append(ngram)
i = j
# Adds tokens that are considered as concepts under the Part Of Speech conditions
elif postags[i] in POS_CONCEPTS and not toks[i].is_stop:
concepts.append(toks[i].lemma_) # May change lemma by text...
i += 1
else:
i += 1
concepts_uri = [
standardized_concept_uri('en', ngram) for ngram in concepts
]
unique_concepts_uri = list(OrderedDict.fromkeys(concepts_uri))
if verbose:
print("")
print("Preprocessing generate_graph")
print("concepts_uri:", concepts_uri)
print("unique_concepts_uri:", unique_concepts_uri)
print("")
for uri_word in tqdm(unique_concepts_uri, total=len(unique_concepts_uri)):
normalized_word = uri_to_label(uri_word)
primary_vocab_list.append(normalized_word)
if normalized_word not in total_vocab_list:
total_vocab_list.append(normalized_word)
if conceptnet_dict == None:
try:
obj = requests.get('{}{}'.format("http://api.conceptnet.io",
uri_word)).json()
edges = obj["edges"]
except Exception:
print("Request error")
edges = []
else:
if normalized_word not in conceptnet_dict:
try:
obj = requests.get('{}{}'.format(
"http://api.conceptnet.io", uri_word)).json()
edges = obj["edges"]
conceptnet_dict[normalized_word] = edges
except Exception:
print("Request error")
edges = []
else:
edges = conceptnet_dict[normalized_word]
at_least_one_edge = False
for edge in edges:
start_node = edge["start"]["term"]
end_node = edge["end"]["term"]
rel = edge["rel"]["label"]
normalized_start_word = uri_to_label(
start_node) # uri_to_label("/c/en/movies") -> "movies"
normalized_end_word = uri_to_label(end_node)
if (rel in DISCARDED_RELATIONS or rel not in RELATIONS
or edge["start"]["language"] != "en"
or edge["end"]["language"] != "en"
or (normalized_start_word in total_vocab_list
and normalized_end_word in total_vocab_list)):
continue
at_least_one_edge = True
if normalized_start_word not in total_vocab_list:
total_vocab_list.append(normalized_start_word)
if normalized_end_word not in total_vocab_list:
total_vocab_list.append(normalized_end_word)
normalized_start_word_idx = total_vocab_list.index(
normalized_start_word)
normalized_end_word_idx = total_vocab_list.index(
normalized_end_word)
edges_list.append(
((normalized_start_word_idx, normalized_end_word_idx),
RELATIONS.index(rel)))
total_vocab_list = [
concept.replace(" ", "_") for concept in total_vocab_list
]
return (total_vocab_list, edges_list)
def generate_graph_for_concept_list(concepts,
conceptnet_dict=None,
verbose=False):
"""Given a list of concepts, and not a sequence, build the KG from
ConceptNet based on this concept list."""
primary_vocab_list = []
total_vocab_list = []
edges_list = []
concepts_uri = [
standardized_concept_uri('en', ngram) for ngram in concepts
]
unique_concepts_uri = list(OrderedDict.fromkeys(concepts_uri))
for uri_word in tqdm(unique_concepts_uri, total=len(unique_concepts_uri)):
normalized_word = uri_to_label(uri_word)
primary_vocab_list.append(normalized_word)
if normalized_word not in total_vocab_list:
total_vocab_list.append(normalized_word)
if conceptnet_dict == None:
try:
obj = requests.get('{}{}'.format("http://api.conceptnet.io",
uri_word)).json()
edges = obj["edges"]
except Exception:
print("Request error")
edges = []
else:
if normalized_word not in conceptnet_dict:
try:
obj = requests.get('{}{}'.format(
"http://api.conceptnet.io", uri_word)).json()
edges = obj["edges"]
conceptnet_dict[normalized_word] = edges
except Exception:
print("Request error")
edges = []
else:
edges = conceptnet_dict[normalized_word]
at_least_one_edge = False
for edge in edges:
start_node = edge["start"]["term"]
end_node = edge["end"]["term"]
rel = edge["rel"]["label"]
normalized_start_word = uri_to_label(
start_node) # uri_to_label("/c/en/movies") -> "movies"
normalized_end_word = uri_to_label(end_node)
if (rel in DISCARDED_RELATIONS or rel not in RELATIONS
or edge["start"]["language"] != "en"
or edge["end"]["language"] != "en"
or (normalized_start_word in total_vocab_list
and normalized_end_word in total_vocab_list)):
continue
at_least_one_edge = True
if normalized_start_word not in total_vocab_list:
total_vocab_list.append(normalized_start_word)
if normalized_end_word not in total_vocab_list:
total_vocab_list.append(normalized_end_word)
normalized_start_word_idx = total_vocab_list.index(
normalized_start_word)
normalized_end_word_idx = total_vocab_list.index(
normalized_end_word)
edges_list.append(
((normalized_start_word_idx, normalized_end_word_idx),
RELATIONS.index(rel)))
total_vocab_list = [
concept.replace(" ", "_") for concept in total_vocab_list
]
return (total_vocab_list, edges_list)
def generate_graph_and_align(toks,
postags,
nertags,
conceptnet_dict=None,
verbose=False):
def _get_concepts(uri_word,
associated_nodes=[],
edges_list=[],
conceptnet_dict=None):
#What about duplicates ??-Don't care, just keep track.
#associated_nodes = []
normalized_word = uri_to_label(uri_word)
normalized_word = uri_to_label(uri_word)
associated_nodes.append(normalized_word)
if conceptnet_dict == None:
try:
obj = requests.get('{}{}'.format("http://api.conceptnet.io",
uri_word)).json()
edges = obj["edges"]
except Exception:
print("Request error")
edges = []
else:
if normalized_word not in conceptnet_dict:
try:
obj = requests.get('{}{}'.format(
"http://api.conceptnet.io", uri_word)).json()
edges = obj["edges"]
conceptnet_dict[normalized_word] = edges
except Exception:
print("Request error")
edges = []
else:
edges = conceptnet_dict[normalized_word]
at_least_one_edge = False
for edge in edges:
start_node = edge["start"]["term"]
end_node = edge["end"]["term"]
rel = edge["rel"]["label"]
normalized_start_word = uri_to_label(
start_node) # uri_to_label("/c/en/movies") -> "movies"
normalized_end_word = uri_to_label(end_node)
if (rel in DISCARDED_RELATIONS or rel not in RELATIONS
or edge["start"]["language"] != "en"
or edge["end"]["language"] != "en"
or (normalized_start_word in associated_nodes
and normalized_end_word in associated_nodes)):
continue
#total_vocab_list-->associated_nodes
at_least_one_edge = True
if normalized_start_word not in associated_nodes:
associated_nodes.append(normalized_start_word)
#total_vocab_list-->associated_node
if normalized_end_word not in associated_nodes:
associated_nodes.append(normalized_end_word)
normalized_start_word_idx = associated_nodes.index(
normalized_start_word)
normalized_end_word_idx = associated_nodes.index(
normalized_end_word)
edges_list.append(
((normalized_start_word_idx, normalized_end_word_idx),
RELATIONS.index(rel)))
#total_vocab_list = [concept.replace(" ", "_") for concept in total_vocab_list]
#total_vocab_
return associated_nodes, edges_list
SEED = 1234
random.seed(SEED)
primary_vocab_list = []
total_vocab_list = []
edges_list = []
SEQ_LIST = []
concepts = []
toks_len = len(toks)
i = 0
while i < toks_len:
# Adds entities (n_grams)
if nertags[i] in ENTITY_BEGINNING:
ngram_list = [toks[i].text]
j = i + 1
while j < toks_len and nertags[j] in ENTITY_INSIDE:
ngram_list.append(toks[j].text)
j += 1
ngram = ' '.join(ngram_list)
concepts.append(ngram)
#modif
uri_word = standardized_concept_uri('en', ngram)
norm_word = uri_to_label(uri_word)
if norm_word in total_vocab_list:
SEQ_LIST.append(
(i, norm_word, total_vocab_list.index(norm_word)))
else:
total_vocab_list, edges_list = _get_concepts(
uri_word, total_vocab_list, edges_list, conceptnet_dict)
SEQ_LIST.append(
(i, norm_word, total_vocab_list.index(norm_word)))
i = j
# Adds tokens that are considered as concepts under the Part Of Speech conditions
elif postags[i] in POS_CONCEPTS and not toks[i].is_stop:
concepts.append(toks[i].lemma_) # May change lemma by text...
lemma = toks[i].lemma_
uri_word = standardized_concept_uri('en', lemma)
norm_word = uri_to_label(uri_word)
if norm_word in total_vocab_list:
SEQ_LIST.append(
(i, norm_word, total_vocab_list.index(norm_word)))
else:
total_vocab_list, edges_list = _get_concepts(
uri_word, total_vocab_list, edges_list, conceptnet_dict)
SEQ_LIST.append(
(i, norm_word, total_vocab_list.index(norm_word)))
i += 1
else:
i += 1
return (total_vocab_list, edges_list), SEQ_LIST
