def handle_file(input_filename, output_file):
out = JSONStreamWriter(output_file)
for line in codecs.open(input_filename, encoding='utf-8'):
line = line.strip()
if line:
for new_obj in handle_raw_assertion(line):
out.write(new_obj)
def handle_file(input_filename, output_file):
out = JSONStreamWriter(output_file)
for line in codecs.open(input_filename, encoding='utf-8'):
line = line.strip()
if line:
for new_obj in handle_raw_assertion(line):
out.write(new_obj)
def msgpack_to_json(input_filename, output_filename):
"""
Convert a msgpack stream to a JSON stream (with one object per line).
"""
out_stream = JSONStreamWriter(output_filename)
for obj in read_msgpack_stream(input_filename):
out_stream.write(obj)
out_stream.close()
def msgpack_to_json(input_filename, output_filename):
"""
Convert a msgpack stream to a JSON stream (with one object per line).
"""
out_stream = JSONStreamWriter(output_filename)
for obj in read_msgpack_stream(input_filename):
out_stream.write(obj)
out_stream.close()
def test_json_to_msgpack():
with TemporaryDirectory(prefix='conceptnet-test') as tmpdir:
json_path = os.path.join(tmpdir, 'test.jsons')
msgpack_path = os.path.join(tmpdir, 'test.msgpack')
writer = JSONStreamWriter(json_path)
for item in DATA:
writer.write(item)
writer.close()
json_to_msgpack(json_path, msgpack_path)
reader = read_msgpack_stream(msgpack_path)
for known, read in zip_longest(DATA, reader):
eq_(known, read)
def test_json_to_msgpack():
with TemporaryDirectory(prefix='conceptnet-test') as tmpdir:
json_path = os.path.join(tmpdir, 'test.jsons')
msgpack_path = os.path.join(tmpdir, 'test.msgpack')
writer = JSONStreamWriter(json_path)
for item in DATA:
writer.write(item)
writer.close()
json_to_msgpack(json_path, msgpack_path)
reader = read_msgpack_stream(msgpack_path)
for known, read in zip_longest(DATA, reader):
eq_(known, read)
def build_from_dir(dirname, output_file):
"""
Read a GlobalMind database exported in YAML files, translate
it into ConceptNet 5 edges, and write those edges to disk using
a JSONStreamWriter.
"""
out = JSONStreamWriter(output_file)
userdata = yaml.load_all(open(dirname + '/GMUser.yaml'))
users = {}
for userinfo in userdata:
users[userinfo['pk']] = userinfo
frame_data = yaml.load_all(open(dirname + '/GMFrame.yaml'))
frames = {}
for frame in frame_data:
frames[frame['pk']] = frame['fields']
assertiondata = yaml.load_all(open(dirname + '/GMAssertion.yaml'))
assertions = {}
for assertion in assertiondata:
obj = assertion['fields']
frame = frames[obj['frame']]
frametext = frame['text']
userinfo = users[obj['author']]
username = userinfo['fields']['username']
# GlobalMind provides information about what country the user is from, which
# we can preserve in the contributor URI.
#
# If I got to re-choose these URIs, I would distinguish usernames with
# a country code from those without a country code by something more
# than the number of slashes, and I would write the country code in
# capital letters.
userlocale = userinfo['fields']['ccode'].lower()
if userlocale:
user_source = "/s/contributor/globalmind/%s/%s" % (userlocale, username)
else:
user_source = "/s/contributor/globalmind/%s" % username
sources = [
user_source,
"/s/activity/globalmind/assert"
]
lang = LANG_CODES[obj['lcode']]
start = normalized_concept_uri(lang, obj['node1'])
end = normalized_concept_uri(lang, obj['node2'])
rel = '/r/' + RELATION_MAP.get(frame['relation'], frame['relation'])
# fix messy english "around in"
if ' around ' in frametext:
if obj['node2'].startswith('in '):
frametext = frametext.replace(' around ', ' in ')
obj['node2'] = obj['node2'][3:]
else:
frametext = frametext.replace(' around ', ' near ')
rel = '/r/LocatedNear'
# fix more awkward English. I wonder how bad the other languages are.
frametext = frametext.replace('hits your head', 'comes to mind')
frametext = frametext.replace(': [node1], [node2]', ' [node1] and [node2]')
node1 = u'[[' + obj['node1'] + u']]'
node2 = u'[[' + obj['node2'] + u']]'
surfaceText = frametext.replace('//', '').replace('[node1]', node1).replace('[node2]', node2)
edge = make_edge(rel, start, end,
dataset='/d/globalmind',
license='/l/CC/By',
sources=sources,
surfaceText=surfaceText,
weight=1)
out.write(edge)
assertions[assertion['pk']] = edge
translationdata = yaml.load_all(open(dirname + '/GMTranslation.yaml'))
for translation in translationdata:
obj = translation['fields']
assertion1 = assertions[obj['assertion1']]
assertion2 = assertions[obj['assertion2']]
start = assertion1['uri']
end = assertion2['uri']
rel = '/r/TranslationOf'
text1 = assertion1['surfaceText'].replace('[[', '').replace(']]', '')
text2 = assertion2['surfaceText'].replace('[[', '').replace(']]', '')
lang1 = LANG_NAMES[get_lang(assertion1)]
lang2 = LANG_NAMES[get_lang(assertion2)]
surfaceText = u"[[%s]] in %s means [[%s]] in %s." % (text1, lang1, text2, lang2)
userinfo = users[obj['author']]
username = userinfo['fields']['username']
userlocale = userinfo['fields']['ccode'].lower()
if userlocale:
user_source = "/s/contributor/globalmind/%s/%s" % (userlocale, username)
else:
user_source = "/s/contributor/globalmind/%s" % username
sources = [
user_source,
"/s/activity/globalmind/translate"
]
edge = make_edge(rel, start, end,
dataset='/d/globalmind',
license=Licenses.cc_attribution,
sources=sources,
surfaceText=surfaceText,
weight=1)
out.write(edge)
def convert_to_json(input_filename, output_filename):
out_stream = JSONStreamWriter(output_filename)
for obj in read_msgpack_stream(input_filename):
out_stream.write(obj)
out_stream.close()
class FindTranslations(ContentHandler):
def __init__(self, output_file='wiktionary.json'):
self.lang = None
self.langcode = None
self.inArticle = False
self.inTitle = False
self.curSense = None
self.curTitle = ''
self.curText = ''
self.locales = []
self.curRelation = None
self.writer = JSONStreamWriter(output_file)
def startElement(self, name, attrs):
if name == 'page':
self.inArticle = True
self.curText = []
elif name == 'title':
self.inTitle = True
self.curTitle = ''
def endElement(self, name):
if name == 'page':
self.inArticle = False
self.handleArticle(self.curTitle, ''.join(self.curText))
elif name == 'title':
self.inTitle = False
def characters(self, text):
if self.inTitle:
self.curTitle += text
elif self.inArticle:
self.curText.append(text)
if len(self.curText) > 10000:
# bail out
self.inArticle = False
def handleArticle(self, title, text):
lines = text.split('\n')
self.pos = None
for line in lines:
self.handleLine(title, line.strip())
def handleLine(self, title, line):
language_match = LANGUAGE_HEADER.match(line)
trans_top_match = TRANS_TOP.match(line)
trans_tag_match = TRANS_TAG.search(line)
chinese_match = CHINESE_TAG.search(line)
if line.startswith('===') and line.endswith('==='):
pos = line.strip('= ')
if pos == 'Synonyms':
self.curRelation = 'Synonym'
elif pos == 'Antonym':
self.curRelation = 'Antonym'
elif pos == 'Related terms':
self.curRelation = 'RelatedTo'
elif pos == 'Derived terms':
if not line.startswith('===='):
# this is at the same level as the part of speech;
# now we don't know what POS these apply to
self.pos = None
self.curRelation = 'DerivedFrom'
else:
self.curRelation = None
if pos in PARTS_OF_SPEECH:
self.pos = PARTS_OF_SPEECH[pos]
elif language_match:
self.lang = language_match.group(1)
self.langcode = LANGUAGES.get(self.lang)
elif chinese_match:
scripttag = chinese_match.group(2)
self.locales = []
if 's' in scripttag:
self.locales.append('_CN')
if 't' in scripttag:
self.locales.append('_TW')
elif line[
0:
1] == '#' and self.lang != 'English' and self.lang is not None:
defn = line[1:].strip()
if defn[0:1] not in ':*#':
for defn2 in filter_line(defn):
if not ascii_enough(defn2): continue
if 'Index:' in title: continue
if self.langcode == 'zh':
for locale in self.locales:
self.output_translation(title, defn2, locale)
elif self.langcode:
self.output_translation(title, defn2)
elif line[0:4] == '----':
self.pos = None
self.lang = None
self.langcode = None
self.curRelation = None
elif trans_top_match:
pos = self.pos or 'n'
sense = trans_top_match.group(1).split(';')[0].strip('.')
if 'translations' in sense.lower():
self.curSense = None
else:
self.curSense = (pos, sense)
elif trans_tag_match:
lang = trans_tag_match.group(1)
translation = trans_tag_match.group(2)
if self.curSense is not None and self.lang == 'English':
# handle Chinese separately
if lang not in ('cmn', 'yue', 'zh-yue', 'zh'):
self.output_sense_translation(lang, translation, title,
self.curSense)
elif '{{trans-bottom}}' in line:
self.curSense = None
elif line.startswith('* ') and self.curRelation and self.langcode:
relatedmatch = WIKILINK.search(line)
if relatedmatch:
related = relatedmatch.group(1)
self.output_monolingual(self.langcode, self.curRelation,
related, title)
def output_monolingual(self, lang, relation, term1, term2):
if term_is_bad(term1) or term_is_bad(term2):
return
source = normalized_concept_uri(lang, term1)
if self.pos:
target = normalized_concept_uri(lang, term2, self.pos)
else:
target = normalized_concept_uri(lang, term2)
surfaceText = "[[%s]] %s [[%s]]" % (term1, relation, term2)
edge = make_edge('/r/' + relation,
source,
target,
'/d/wiktionary/%s/%s' % (lang, lang),
license=Licenses.cc_sharealike,
sources=[SOURCE, MONOLINGUAL],
weight=1.0,
surfaceText=surfaceText)
self.writer.write(edge)
def output_sense_translation(self, lang, foreign, english, sense):
pos, disambiguation = sense
if 'Wik' in foreign or 'Wik' in english or term_is_bad(
foreign) or term_is_bad(english):
return
# Quick fix that drops definitions written in Lojban syntax
if lang == 'jbo' and re.search(r'x[1-5]', english):
return
if lang == 'zh-cn':
lang = 'zh_CN'
elif lang == 'zh-tw':
lang = 'zh_TW'
source = normalized_concept_uri(lang,
unicodedata.normalize('NFKC', foreign))
target = normalized_concept_uri('en', english, pos, disambiguation)
relation = '/r/TranslationOf'
try:
surfaceRel = "is %s for" % (
CODE_TO_ENGLISH_NAME[lang.split('_')[0]])
except KeyError:
surfaceRel = "is [language %s] for" % lang
surfaceText = "[[%s]] %s [[%s (%s)]]" % (
foreign, surfaceRel, english,
disambiguation.split('/')[-1].replace('_', ' '))
edge = make_edge(relation,
source,
target,
'/d/wiktionary/en/%s' % lang,
license=Licenses.cc_sharealike,
sources=[SOURCE, TRANSLATE],
weight=1.0,
surfaceText=surfaceText)
self.writer.write(edge)
def output_translation(self, foreign, english, locale=''):
if term_is_bad(foreign) or term_is_bad(english):
return
# Quick fix that drops definitions written in Lojban syntax
if self.langcode == 'jbo' and re.search(r'x[1-5]', english):
return
source = normalized_concept_uri(self.langcode + locale, foreign)
target = normalized_concept_uri('en', english)
relation = '/r/TranslationOf'
try:
surfaceRel = "is %s for" % (
CODE_TO_ENGLISH_NAME[self.langcode.split('_')[0]])
except KeyError:
surfaceRel = "is [language %s] for" % self.langcode
surfaceText = "[[%s]] %s [[%s]]" % (foreign, surfaceRel, english)
edge = make_edge(relation,
source,
target,
'/d/wiktionary/en/%s' % self.langcode,
license=Licenses.cc_sharealike,
sources=[SOURCE, INTERLINGUAL],
weight=1.0,
surfaceText=surfaceText)
self.writer.write(edge)
class FindTranslations(ContentHandler):
def __init__(self, output_file='wiktionary.json'):
self.lang = None
self.langcode = None
self.inArticle = False
self.inTitle = False
self.curSense = None
self.curTitle = ''
self.curText = ''
self.locales = []
self.curRelation = None
self.writer = JSONStreamWriter(output_file)
def startElement(self, name, attrs):
if name == 'page':
self.inArticle = True
self.curText = []
elif name == 'title':
self.inTitle = True
self.curTitle = ''
def endElement(self, name):
if name == 'page':
self.inArticle = False
self.handleArticle(self.curTitle, ''.join(self.curText))
elif name == 'title':
self.inTitle = False
def characters(self, text):
if self.inTitle:
self.curTitle += text
elif self.inArticle:
self.curText.append(text)
if len(self.curText) > 10000:
# bail out
self.inArticle = False
def handleArticle(self, title, text):
lines = text.split('\n')
self.pos = None
for line in lines:
self.handleLine(title, line.strip())
def handleLine(self, title, line):
language_match = LANGUAGE_HEADER.match(line)
trans_top_match = TRANS_TOP.match(line)
trans_tag_match = TRANS_TAG.search(line)
chinese_match = CHINESE_TAG.search(line)
if line.startswith('===') and line.endswith('==='):
pos = line.strip('= ')
if pos == 'Synonyms':
self.curRelation = 'Synonym'
elif pos == 'Antonym':
self.curRelation = 'Antonym'
elif pos == 'Related terms':
self.curRelation = 'RelatedTo'
elif pos == 'Derived terms':
if not line.startswith('===='):
# this is at the same level as the part of speech;
# now we don't know what POS these apply to
self.pos = None
self.curRelation = 'DerivedFrom'
else:
self.curRelation = None
if pos in PARTS_OF_SPEECH:
self.pos = PARTS_OF_SPEECH[pos]
elif language_match:
self.lang = language_match.group(1)
self.langcode = LANGUAGES.get(self.lang)
elif chinese_match:
scripttag = chinese_match.group(2)
self.locales = []
if 's' in scripttag:
self.locales.append('_CN')
if 't' in scripttag:
self.locales.append('_TW')
elif line[0:1] == '#' and self.lang != 'English' and self.lang is not None:
defn = line[1:].strip()
if defn[0:1] not in ':*#':
for defn2 in filter_line(defn):
if not ascii_enough(defn2): continue
if 'Index:' in title: continue
if self.langcode == 'zh':
for locale in self.locales:
self.output_translation(title, defn2, locale)
elif self.langcode:
self.output_translation(title, defn2)
elif line[0:4] == '----':
self.pos = None
self.lang = None
self.langcode = None
self.curRelation = None
elif trans_top_match:
pos = self.pos or 'n'
sense = trans_top_match.group(1).split(';')[0].strip('.')
if 'translations' in sense.lower():
self.curSense = None
else:
self.curSense = (pos, sense)
elif trans_tag_match:
lang = trans_tag_match.group(1)
translation = trans_tag_match.group(2)
if self.curSense is not None and self.lang == 'English':
# handle Chinese separately
if lang not in ('cmn', 'yue', 'zh-yue', 'zh'):
self.output_sense_translation(lang, translation, title,
self.curSense)
elif '{{trans-bottom}}' in line:
self.curSense = None
elif line.startswith('* ') and self.curRelation and self.langcode:
relatedmatch = WIKILINK.search(line)
if relatedmatch:
related = relatedmatch.group(1)
self.output_monolingual(self.langcode, self.curRelation,
related, title)
def output_monolingual(self, lang, relation, term1, term2):
if term_is_bad(term1) or term_is_bad(term2):
return
source = normalized_concept_uri(lang, term1)
if self.pos:
target = normalized_concept_uri(lang, term2, self.pos)
else:
target = normalized_concept_uri(lang, term2)
surfaceText = "[[%s]] %s [[%s]]" % (term1, relation, term2)
edge = make_edge('/r/'+relation, source, target, '/d/wiktionary/%s/%s' % (lang, lang),
license=Licenses.cc_sharealike,
sources=[SOURCE, MONOLINGUAL],
weight=1.0,
surfaceText=surfaceText)
self.writer.write(edge)
def output_sense_translation(self, lang, foreign, english, sense):
pos, disambiguation = sense
if 'Wik' in foreign or 'Wik' in english or term_is_bad(foreign) or term_is_bad(english):
return
# Quick fix that drops definitions written in Lojban syntax
if lang == 'jbo' and re.search(r'x[1-5]', english):
return
if lang == 'zh-cn':
lang = 'zh_CN'
elif lang == 'zh-tw':
lang = 'zh_TW'
source = normalized_concept_uri(
lang, unicodedata.normalize('NFKC', foreign)
)
target = normalized_concept_uri(
'en', english, pos, disambiguation
)
relation = '/r/TranslationOf'
try:
surfaceRel = "is %s for" % (CODE_TO_ENGLISH_NAME[lang.split('_')[0]])
except KeyError:
surfaceRel = "is [language %s] for" % lang
surfaceText = "[[%s]] %s [[%s (%s)]]" % (foreign, surfaceRel, english, disambiguation.split('/')[-1].replace('_', ' '))
edge = make_edge(relation, source, target, '/d/wiktionary/en/%s' % lang,
license=Licenses.cc_sharealike,
sources=[SOURCE, TRANSLATE],
weight=1.0,
surfaceText=surfaceText)
self.writer.write(edge)
def output_translation(self, foreign, english, locale=''):
if term_is_bad(foreign) or term_is_bad(english):
return
# Quick fix that drops definitions written in Lojban syntax
if self.langcode == 'jbo' and re.search(r'x[1-5]', english):
return
source = normalized_concept_uri(
self.langcode + locale,
foreign
)
target = normalized_concept_uri(
'en', english
)
relation = '/r/TranslationOf'
try:
surfaceRel = "is %s for" % (CODE_TO_ENGLISH_NAME[self.langcode.split('_')[0]])
except KeyError:
surfaceRel = "is [language %s] for" % self.langcode
surfaceText = "[[%s]] %s [[%s]]" % (foreign, surfaceRel, english)
edge = make_edge(relation, source, target, '/d/wiktionary/en/%s' % self.langcode,
license=Licenses.cc_sharealike,
sources=[SOURCE, INTERLINGUAL],
weight=1.0,
surfaceText=surfaceText)
self.writer.write(edge)
def handle_file(infile, outfile):
count = 0
outcomes = defaultdict(int)
sources = ['/s/site/verbosity']
writer = JSONStreamWriter(outfile)
for line in open(infile):
parts = line.strip().split('\t')
if not parts:
outcomes['blank'] += 1
continue
# The first 5 columns of the Verbosity output file are:
#
# left: the word being clued
# relation: the relation between the word and the clue that the
# clue-giver chose, in a form such as "it is part of"
# right: the one or two words used as the clue
# freq: the number of different times this clue was given
# orderscore: the average position in the list of clues
#
# 'orderscore' is a number from 0 to 999, representing the average
# quantile of its position in the list of clues. (It's like a
# percentile, except there are 1000 of them, not 100.)
#
# A clue that's always given first has an orderscore of 0. A clue
# that always appears halfway through the list has an orderscore of
# 500.
#
# This may seem like a strange thing to measure, and I didn't come up
# with it, but it actually turns out to be somewhat informative.
# A clue with an orderscore of 0 is probably a good common-sense
# relation, representing the first thing that comes to mind. A clue
# with a high order score may be a move of desperation after several
# other clues have failed. It causes the guesser to get the answer
# soon afterward, but perhaps because it's a "cheating" move. So,
# low orderscores represent better common sense relations.
left, relation, right, freq, orderscore = parts[:5]
freq = int(freq)
orderscore = int(orderscore)
# Test each word
flagged = False
for rword in right.split():
if BAD_CLUE_REGEX.match(rword):
flagged = True
break
if flagged:
outcomes['flag word'] += 1
continue
if len(right) < 3:
outcomes['clue too short'] += 1
continue
if len(right.split()[-1]) == 1:
outcomes['letter'] += 1
continue
# The Verbosity interface and gameplay did not particularly encourage
# players to choose an appropriate relation. In practice, players seem
# to have used them all interchangeably, except for the negative
# relation "it is the opposite of", expressing /r/Antonym.
#
# Another way that players expressed negative relations was to use
# 'not' as the first word of their clue; we make that into an instance
# of /r/Antonym as well.
#
# In other cases, the relation is a positive relation, so we replace it
# with the most general positive relation, /r/RelatedTo.
rel = '/r/RelatedTo'
reltext = 'is related to'
if right.startswith('not '):
rel = '/r/Antonym'
right = right[4:]
reltext = 'is not'
if relation == 'it is the opposite of':
rel = '/r/Antonym'
reltext = 'is the opposite of'
# The "sounds-like score" determines whether this clue seems to be a
# pun or rhyme, rather than an actual common-sense relationship. If
# the sounds-like score is over 0.35, skip the assertion.
sls = sounds_like_score(left, right)
if sls > 0.35:
outcomes['text similarity'] += 1
continue
# Calculate a score for the assertion:
#
# - The number of times it's been used as a clue
# - ...with a linear penalty for a high sounds-like score
# - ...and a linear penalty for high orderscores
#
# The penalties are multiplicative factors from 0 to 1, which decrease
# linearly as the relevant penalties increase. If a clue is given N
# times, with a sounds-like score of 0 and an orderscore of 0, it will
# get an overall score of 2N - 1. This is a formula we should probably
# revisit.
#
# The weight is the score divided by 100. All divisions are floating
# point, as defined by the __future__ import at the top of this module.
score = (freq * 2 - 1) * (1 - sls) * (1 - orderscore / 1000)
if score <= 0.5:
outcomes['low score'] += 1
continue
weight = score / 100
# If the clue on the right is a two-word phrase, we make additional
# connections to both words individually. We label them with the
# rule-based source '/s/rule/split_words' to track that this happened.
rightwords = [right]
if ' ' in right:
morewords = [word for word in right.split(' ') if word not in STOPWORDS]
rightwords.extend(morewords)
for i, rightword in enumerate(rightwords):
edge_sources = list(sources)
if i > 0:
edge_sources.append('/s/rule/split_words')
# Build the natural-language-ish surface text for this clue
text = '[[%s]] %s [[%s]]' % (left, reltext, rightword)
count += 1
outcomes['success'] += 1
leftc = normalized_concept_uri('en', left)
rightc = normalized_concept_uri('en', rightword)
edge = make_edge(rel, leftc, rightc, dataset='/d/verbosity',
license=Licenses.cc_attribution,
sources=sources, surfaceText=text,
weight=weight)
writer.write(edge)
# Count the various outcomes. This can be used as a sanity-check. It
# also was used for a graph in a ConceptNet 5 paper.
print("Verbosity outcomes: %s" % outcomes)
def run_wordnet(input_dir, output_file, sw_map_file):
out = JSONStreamWriter(output_file)
map_out = NTriplesWriter(sw_map_file)
reader = NTriplesReader()
synset_senses = defaultdict(list)
sense_synsets = {}
labels = {}
glossary = {}
concept_map = {}
sense_to_synset = {}
# Parse lines such as:
# wn30:synset-Aeolian-noun-2 rdfs:label "Aeolian"@en-us .
for subj, rel, obj, objtag in reader.parse_file(os.path.join(input_dir, 'wordnet-synset.ttl')):
if resource_name(rel) == 'label':
# Everything in WordNet is in English
assert objtag == 'en'
labels[subj] = obj
for subj, rel, obj, objtag in reader.parse_file(os.path.join(input_dir, 'wordnet-glossary.ttl')):
if resource_name(rel) == 'gloss':
assert objtag == 'en'
# Take the definition up to the first semicolon
text = obj.split(';')[0]
# Remove introductory phrases with a colon
text = text.split(': ', 1)[-1]
# Remove parenthesized expressions
while True:
newtext = re.sub(r'\(.+?\) ?', '', text).strip()
if newtext == text or newtext == '':
break
else:
text = newtext
glossary[subj] = text.replace('/', '_')
# Get the list of word senses in each synset, and make a bidirectional mapping.
#
# Example line:
# wn30:synset-Aeolian-noun-2 wn20schema:containsWordSense wn30:wordsense-Aeolian-noun-2 .
for subj, rel, obj, objtag in reader.parse_file(os.path.join(input_dir, 'full/wordnet-wordsense-synset-relations.ttl')):
if resource_name(rel) == 'containsWordSense':
synset_senses[subj].append(obj)
sense_synsets[obj] = subj
# Assign every synset to a disambiguated concept.
for synset in synset_senses:
synset_name = labels[synset]
synset_pos = synset.split('-')[-2]
pos = PARTS_OF_SPEECH[synset_pos]
disambig = glossary[synset]
concept = normalized_concept_uri('en', synset_name, pos, disambig)
concept_map[synset] = concept
# Map senses to their synsets.
for sense, synset in sense_synsets.items():
sense_to_synset[sense] = synset
for filename in (
'wordnet-attribute.ttl', 'wordnet-causes.ttl',
'wordnet-classifiedby.ttl', 'wordnet-entailment.ttl',
'wordnet-hyponym.ttl', 'wordnet-instances.ttl',
'wordnet-membermeronym.ttl', 'wordnet-partmeronym.ttl',
'wordnet-sameverbgroupas.ttl', 'wordnet-similarity.ttl',
'wordnet-substancemeronym.ttl', 'full/wordnet-antonym.ttl',
'full/wordnet-derivationallyrelated.ttl',
'full/wordnet-participleof.ttl',
'full/wordnet-pertainsto.ttl',
'full/wordnet-seealso.ttl'
):
filepath = os.path.join(input_dir, filename)
if os.path.exists(filepath):
for web_subj, web_rel, web_obj, objtag in reader.parse_file(filepath):
# If this relation involves word senses, map them to their synsets
# first.
if web_subj in sense_to_synset:
web_subj = sense_to_synset[web_subj]
if web_obj in sense_to_synset:
web_obj = sense_to_synset[web_obj]
subj = concept_map[web_subj]
obj = concept_map[web_obj]
pred_label = resource_name(web_rel)
if pred_label in REL_MAPPING:
mapped_rel = REL_MAPPING[pred_label]
# Handle WordNet relations that are the reverse of ConceptNet
# relations. Change the word 'meronym' to 'holonym' if
# necessary.
if mapped_rel.startswith('~'):
subj, obj = obj, subj
web_subj, web_obj = web_obj, web_subj
web_rel = web_rel.replace('meronym', 'holonym')
mapped_rel = mapped_rel[1:]
rel = join_uri('r', mapped_rel)
else:
rel = join_uri('r', 'wordnet', pred_label)
map_out.write_link(web_rel, full_conceptnet_url(rel))
map_out.write_link(web_subj, full_conceptnet_url(subj))
map_out.write_link(web_obj, full_conceptnet_url(obj))
edge = make_edge(
rel, subj, obj, dataset='/d/wordnet/3.0',
license='/l/CC/By', sources=SOURCE, weight=2.0
)
out.write(edge)
def build_from_dir(dirname, output_file):
"""
Read a GlobalMind database exported in YAML files, translate
it into ConceptNet 5 edges, and write those edges to disk using
a JSONStreamWriter.
"""
out = JSONStreamWriter(output_file)
userdata = yaml.load_all(open(dirname + '/GMUser.yaml'))
users = {}
for userinfo in userdata:
users[userinfo['pk']] = userinfo
frame_data = yaml.load_all(open(dirname + '/GMFrame.yaml'))
frames = {}
for frame in frame_data:
frames[frame['pk']] = frame['fields']
assertiondata = yaml.load_all(open(dirname + '/GMAssertion.yaml'))
assertions = {}
for assertion in assertiondata:
obj = assertion['fields']
frame = frames[obj['frame']]
frametext = frame['text']
userinfo = users[obj['author']]
username = userinfo['fields']['username']
# GlobalMind provides information about what country the user is from, which
# we can preserve in the contributor URI.
#
# If I got to re-choose these URIs, I would distinguish usernames with
# a country code from those without a country code by something more
# than the number of slashes, and I would write the country code in
# capital letters.
userlocale = userinfo['fields']['ccode'].lower()
if userlocale:
user_source = "/s/contributor/globalmind/%s/%s" % (userlocale,
username)
else:
user_source = "/s/contributor/globalmind/%s" % username
sources = [user_source, "/s/activity/globalmind/assert"]
lang = LANG_CODES[obj['lcode']]
start = normalized_concept_uri(lang, obj['node1'])
end = normalized_concept_uri(lang, obj['node2'])
rel = '/r/' + RELATION_MAP.get(frame['relation'], frame['relation'])
# fix messy english "around in"
if ' around ' in frametext:
if obj['node2'].startswith('in '):
frametext = frametext.replace(' around ', ' in ')
obj['node2'] = obj['node2'][3:]
else:
frametext = frametext.replace(' around ', ' near ')
rel = '/r/LocatedNear'
# fix more awkward English. I wonder how bad the other languages are.
frametext = frametext.replace('hits your head', 'comes to mind')
frametext = frametext.replace(': [node1], [node2]',
' [node1] and [node2]')
node1 = u'[[' + obj['node1'] + u']]'
node2 = u'[[' + obj['node2'] + u']]'
surfaceText = frametext.replace('//', '').replace('[node1]',
node1).replace(
'[node2]', node2)
edge = make_edge(rel,
start,
end,
dataset='/d/globalmind',
license='/l/CC/By',
sources=sources,
surfaceText=surfaceText,
weight=1)
out.write(edge)
assertions[assertion['pk']] = edge
translationdata = yaml.load_all(open(dirname + '/GMTranslation.yaml'))
for translation in translationdata:
obj = translation['fields']
assertion1 = assertions[obj['assertion1']]
assertion2 = assertions[obj['assertion2']]
start = assertion1['uri']
end = assertion2['uri']
rel = '/r/TranslationOf'
text1 = assertion1['surfaceText'].replace('[[', '').replace(']]', '')
text2 = assertion2['surfaceText'].replace('[[', '').replace(']]', '')
lang1 = LANG_NAMES[get_lang(assertion1)]
lang2 = LANG_NAMES[get_lang(assertion2)]
surfaceText = u"[[%s]] in %s means [[%s]] in %s." % (text1, lang1,
text2, lang2)
userinfo = users[obj['author']]
username = userinfo['fields']['username']
userlocale = userinfo['fields']['ccode'].lower()
if userlocale:
user_source = "/s/contributor/globalmind/%s/%s" % (userlocale,
username)
else:
user_source = "/s/contributor/globalmind/%s" % username
sources = [user_source, "/s/activity/globalmind/translate"]
edge = make_edge(rel,
start,
end,
dataset='/d/globalmind',
license=Licenses.cc_attribution,
sources=sources,
surfaceText=surfaceText,
weight=1)
out.write(edge)
def transform_file(self, input_filename, output_file):
out = JSONStreamWriter(output_file)
for obj in read_json_stream(input_filename):
for new_obj in self.handle_assertion(obj):
out.write(new_obj)
def msgpack_to_json(input_filename, output_filename):
out_stream = JSONStreamWriter(output_filename)
for obj in read_msgpack_stream(input_filename):
out_stream.write(obj)
out_stream.close()
def handle_file(infile, outfile):
count = 0
outcomes = defaultdict(int)
sources = ['/s/site/verbosity']
writer = JSONStreamWriter(outfile)
for line in open(infile):
parts = line.strip().split('\t')
if not parts:
outcomes['blank'] += 1
continue
# The first 5 columns of the Verbosity output file are:
#
# left: the word being clued
# relation: the relation between the word and the clue that the
# clue-giver chose, in a form such as "it is part of"
# right: the one or two words used as the clue
# freq: the number of different times this clue was given
# orderscore: the average position in the list of clues
#
# 'orderscore' is a number from 0 to 999, representing the average
# quantile of its position in the list of clues. (It's like a
# percentile, except there are 1000 of them, not 100.)
#
# A clue that's always given first has an orderscore of 0. A clue
# that always appears halfway through the list has an orderscore of
# 500.
#
# This may seem like a strange thing to measure, and I didn't come up
# with it, but it actually turns out to be somewhat informative.
# A clue with an orderscore of 0 is probably a good common-sense
# relation, representing the first thing that comes to mind. A clue
# with a high order score may be a move of desperation after several
# other clues have failed. It causes the guesser to get the answer
# soon afterward, but perhaps because it's a "cheating" move. So,
# low orderscores represent better common sense relations.
left, relation, right, freq, orderscore = parts[:5]
freq = int(freq)
orderscore = int(orderscore)
# Test each word
flagged = False
for rword in right.split():
if BAD_CLUE_REGEX.match(rword):
flagged = True
break
if flagged:
outcomes['flag word'] += 1
continue
if len(right) < 3:
outcomes['clue too short'] += 1
continue
if len(right.split()[-1]) == 1:
outcomes['letter'] += 1
continue
# The Verbosity interface and gameplay did not particularly encourage
# players to choose an appropriate relation. In practice, players seem
# to have used them all interchangeably, except for the negative
# relation "it is the opposite of", expressing /r/Antonym.
#
# Another way that players expressed negative relations was to use
# 'not' as the first word of their clue; we make that into an instance
# of /r/Antonym as well.
#
# In other cases, the relation is a positive relation, so we replace it
# with the most general positive relation, /r/RelatedTo.
rel = '/r/RelatedTo'
reltext = 'is related to'
if right.startswith('not '):
rel = '/r/Antonym'
right = right[4:]
reltext = 'is not'
if relation == 'it is the opposite of':
rel = '/r/Antonym'
reltext = 'is the opposite of'
# The "sounds-like score" determines whether this clue seems to be a
# pun or rhyme, rather than an actual common-sense relationship. If
# the sounds-like score is over 0.35, skip the assertion.
sls = sounds_like_score(left, right)
if sls > 0.35:
outcomes['text similarity'] += 1
continue
# Calculate a score for the assertion:
#
# - The number of times it's been used as a clue
# - ...with a linear penalty for a high sounds-like score
# - ...and a linear penalty for high orderscores
#
# The penalties are multiplicative factors from 0 to 1, which decrease
# linearly as the relevant penalties increase. If a clue is given N
# times, with a sounds-like score of 0 and an orderscore of 0, it will
# get an overall score of 2N - 1. This is a formula we should probably
# revisit.
#
# The weight is the score divided by 100. All divisions are floating
# point, as defined by the __future__ import at the top of this module.
score = (freq * 2 - 1) * (1 - sls) * (1 - orderscore / 1000)
if score <= 0.5:
outcomes['low score'] += 1
continue
weight = score / 100
# If the clue on the right is a two-word phrase, we make additional
# connections to both words individually. We label them with the
# rule-based source '/s/rule/split_words' to track that this happened.
rightwords = [right]
if ' ' in right:
morewords = [
word for word in right.split(' ') if word not in STOPWORDS
]
rightwords.extend(morewords)
for i, rightword in enumerate(rightwords):
edge_sources = list(sources)
if i > 0:
edge_sources.append('/s/rule/split_words')
# Build the natural-language-ish surface text for this clue
text = '[[%s]] %s [[%s]]' % (left, reltext, rightword)
count += 1
outcomes['success'] += 1
leftc = normalized_concept_uri('en', left)
rightc = normalized_concept_uri('en', rightword)
edge = make_edge(rel,
leftc,
rightc,
dataset='/d/verbosity',
license=Licenses.cc_attribution,
sources=sources,
surfaceText=text,
weight=weight)
writer.write(edge)
# Count the various outcomes. This can be used as a sanity-check. It
# also was used for a graph in a ConceptNet 5 paper.
print("Verbosity outcomes: %s" % outcomes)
def run_wordnet(input_dir, output_file, sw_map_file):
out = JSONStreamWriter(output_file)
map_out = NTriplesWriter(sw_map_file)
reader = NTriplesReader()
synset_senses = defaultdict(list)
sense_synsets = {}
labels = {}
glossary = {}
concept_map = {}
sense_to_synset = {}
# Parse lines such as:
# wn30:synset-Aeolian-noun-2 rdfs:label "Aeolian"@en-us .
for subj, rel, obj, objtag in reader.parse_file(
os.path.join(input_dir, 'wordnet-synset.ttl')):
if resource_name(rel) == 'label':
# Everything in WordNet is in English
assert objtag == 'en'
labels[subj] = obj
for subj, rel, obj, objtag in reader.parse_file(
os.path.join(input_dir, 'wordnet-glossary.ttl')):
if resource_name(rel) == 'gloss':
assert objtag == 'en'
# Take the definition up to the first semicolon
text = obj.split(';')[0]
# Remove introductory phrases with a colon
text = text.split(': ', 1)[-1]
# Remove parenthesized expressions
while True:
newtext = re.sub(r'\(.+?\) ?', '', text).strip()
if newtext == text or newtext == '':
break
else:
text = newtext
glossary[subj] = text.replace('/', '_')
# Get the list of word senses in each synset, and make a bidirectional mapping.
#
# Example line:
# wn30:synset-Aeolian-noun-2 wn20schema:containsWordSense wn30:wordsense-Aeolian-noun-2 .
for subj, rel, obj, objtag in reader.parse_file(
os.path.join(input_dir,
'full/wordnet-wordsense-synset-relations.ttl')):
if resource_name(rel) == 'containsWordSense':
synset_senses[subj].append(obj)
sense_synsets[obj] = subj
# Assign every synset to a disambiguated concept.
for synset in synset_senses:
synset_name = labels[synset]
synset_pos = synset.split('-')[-2]
pos = PARTS_OF_SPEECH[synset_pos]
disambig = glossary[synset]
concept = normalized_concept_uri('en', synset_name, pos, disambig)
concept_map[synset] = concept
# Map senses to their synsets.
for sense, synset in sense_synsets.items():
sense_to_synset[sense] = synset
for filename in ('wordnet-attribute.ttl', 'wordnet-causes.ttl',
'wordnet-classifiedby.ttl', 'wordnet-entailment.ttl',
'wordnet-hyponym.ttl', 'wordnet-instances.ttl',
'wordnet-membermeronym.ttl', 'wordnet-partmeronym.ttl',
'wordnet-sameverbgroupas.ttl', 'wordnet-similarity.ttl',
'wordnet-substancemeronym.ttl',
'full/wordnet-antonym.ttl',
'full/wordnet-derivationallyrelated.ttl',
'full/wordnet-participleof.ttl',
'full/wordnet-pertainsto.ttl',
'full/wordnet-seealso.ttl'):
filepath = os.path.join(input_dir, filename)
if os.path.exists(filepath):
for web_subj, web_rel, web_obj, objtag in reader.parse_file(
filepath):
# If this relation involves word senses, map them to their synsets
# first.
if web_subj in sense_to_synset:
web_subj = sense_to_synset[web_subj]
if web_obj in sense_to_synset:
web_obj = sense_to_synset[web_obj]
subj = concept_map[web_subj]
obj = concept_map[web_obj]
pred_label = resource_name(web_rel)
if pred_label in REL_MAPPING:
mapped_rel = REL_MAPPING[pred_label]
# Handle WordNet relations that are the reverse of ConceptNet
# relations. Change the word 'meronym' to 'holonym' if
# necessary.
if mapped_rel.startswith('~'):
subj, obj = obj, subj
web_subj, web_obj = web_obj, web_subj
web_rel = web_rel.replace('meronym', 'holonym')
mapped_rel = mapped_rel[1:]
rel = join_uri('r', mapped_rel)
else:
rel = join_uri('r', 'wordnet', pred_label)
map_out.write_link(web_rel, full_conceptnet_url(rel))
map_out.write_link(web_subj, full_conceptnet_url(subj))
map_out.write_link(web_obj, full_conceptnet_url(obj))
edge = make_edge(rel,
subj,
obj,
dataset='/d/wordnet/3.0',
license='/l/CC/By',
sources=SOURCE,
weight=2.0)
out.write(edge)
