def combine_assertions(input_filename, output_file):
"""
Take in a tab-separated, sorted "CSV" files, indicated by
`input_filename`, that should be grouped together into assertions.
Output a msgpack stream of assertions to `output_file`.
The input file should be made from multiple sources of assertions by
concatenating and sorting them.
The combined assertions will all have the dataset of the first edge that
produces them, and the license of the strongest license being combined.
This process requires its input to be a sorted CSV so that all edges for
the same assertion will appear consecutively.
"""
def group_func(line):
"Group lines by their URI (their first column)."
return line.split('\t', 1)[0]
out = MsgpackStreamWriter(output_file)
out_bad = MsgpackStreamWriter(output_file + '.reject')
with open(input_filename, encoding='utf-8') as stream:
for key, line_group in itertools.groupby(stream, group_func):
assertion = make_assertion(line_group)
if assertion is None:
continue
if assertion['weight'] > 0:
destination = out
else:
destination = out_bad
destination.write(assertion)
out.close()
out_bad.close()
def combine_assertions(input_filename, output_file):
"""
Take in a tab-separated, sorted "CSV" files, indicated by
`input_filename`, that should be grouped together into assertions.
Output a msgpack stream of assertions to `output_file`.
The input file should be made from multiple sources of assertions by
concatenating and sorting them.
The combined assertions will all have the dataset of the first edge that
produces them, and the license of the strongest license being combined.
This process requires its input to be a sorted CSV so that all edges for
the same assertion will appear consecutively.
"""
def group_func(line):
"Group lines by their URI (their first column)."
return line.split('\t', 1)[0]
out = MsgpackStreamWriter(output_file)
out_bad = MsgpackStreamWriter(output_file + '.reject')
with open(input_filename, encoding='utf-8') as stream:
for key, line_group in itertools.groupby(stream, group_func):
assertion = make_assertion(line_group)
if assertion is None:
continue
if assertion['weight'] > 0:
destination = out
else:
destination = out_bad
destination.write(assertion)
out.close()
out_bad.close()
def subwords_to_edges(language, input, output):
"""
Morfessor hypothesizes ways to break words into sub-word chunks. Produce
edges from these sub-words that can be used in retrofitting.
"""
writer = MsgpackStreamWriter(output)
for line in input:
line = line.rstrip()
if not line or line.startswith('#'):
continue
# Remove the unnecessary count ("1 ") from the start of each line
line = line.split(' ', 1)[1]
chunks = line.split(' + ')
# Strip a possible trailing underscore, which would particularly show
# up in the way we segment ATOMIC_SPACE_LANGUAGES (Vietnamese)
full_text = ''.join(chunks).strip('_')
end = join_uri('c', language, full_text)
for chunk in chunks:
if chunk != '_':
start = join_uri('x', language, chunk.strip('_'))
edge = make_edge(
'/r/SubwordOf',
start,
end,
dataset='/d/morphology',
license=Licenses.cc_attribution,
sources=MORPH_SOURCES,
weight=0.01,
)
writer.write(edge)
writer.close()
def run_opencyc(input_file, output_file):
"""
Read an .nq file containing OpenCyc data, outputting a file of
ConceptNet edges and a file of mappings between the Semantic Web and
ConceptNet.
"""
out = MsgpackStreamWriter(output_file)
labels = {}
unlabels = defaultdict(set)
seen_external_urls = set()
# Read through the file once, finding all the "preferred labels". We will
# use these as the surface texts for the nodes.
for subj, pred, obj, _graph in parse_nquads(
open(input_file, encoding='utf-8')):
if pred['url'] == RDF_LABEL:
labels[subj['url']] = obj['text']
unlabels[obj['text']].add(subj['url'])
# Read through the file again and extract ConceptNet edges.
for subj, pred, obj, _graph in parse_nquads(
open(input_file, encoding='utf-8')):
rel_name = resource_name(pred['url'])
web_subj = subj.get('url')
web_obj = obj.get('url')
if rel_name == 'subClassOf' and web_obj is not None and web_subj in labels and web_obj in labels:
subj_label = labels[web_subj]
obj_label = labels[web_obj]
if '_' in subj_label or '_' in obj_label:
continue
if subj_label.startswith('xsd:') or obj_label.startswith('xsd:'):
continue
subj_words = set(simple_tokenize(subj_label))
obj_words = set(simple_tokenize(obj_label))
if (subj_words & BLACKLIST_WORDS) or (obj_words & BLACKLIST_WORDS):
continue
if len(subj_words) > 4 or len(obj_words) > 4:
continue
subj_uri = cyc_to_conceptnet_uri(labels, unlabels, web_subj)
obj_uri = cyc_to_conceptnet_uri(labels, unlabels, web_obj)
out.write(
opencyc_edge('/r/IsA', subj_uri, obj_uri, subj_label,
obj_label))
if (subj_uri, web_subj) not in seen_external_urls:
out.write(external_url_edge(subj_uri, web_subj))
seen_external_urls.add((subj_uri, web_subj))
if (obj_uri, web_obj) not in seen_external_urls:
out.write(external_url_edge(obj_uri, web_obj))
seen_external_urls.add((obj_uri, web_obj))
elif rel_name == 'sameAs' and web_subj in labels and web_obj.startswith(
'http://umbel.org/'):
subj_label = labels[web_subj]
subj_uri = standardized_concept_uri('en', subj_label)
if (subj_uri, web_obj) not in seen_external_urls:
out.write(external_url_edge(subj_uri, web_obj))
seen_external_urls.add((subj_uri, web_obj))
out.close()
def json_to_msgpack(input_filename, output_filename):
"""
Convert a JSON stream (with one object per line) to a msgpack stream.
"""
out_stream = MsgpackStreamWriter(output_filename)
for obj in read_json_stream(input_filename):
out_stream.write(obj)
out_stream.close()
def json_to_msgpack(input_filename, output_filename):
"""
Convert a JSON stream (with one object per line) to a msgpack stream.
"""
out_stream = MsgpackStreamWriter(output_filename)
for obj in read_json_stream(input_filename):
out_stream.write(obj)
out_stream.close()
def combine_assertions(input_filename, core_filename, output_filename):
"""
Take in a tab-separated, sorted "CSV" files, indicated by
`input_filename`, that should be grouped together into assertions.
Output a msgpack stream of assertions the file indicated by
`output_filename`.
The input file should be made from multiple sources of assertions by
concatenating and sorting them.
The combined assertions will all have the dataset of the first edge that
produces them, and the license of the strongest license being combined.
This process requires its input to be a sorted CSV so that all edges for
the same assertion will appear consecutively.
"""
def group_func(line):
"Group lines by their URI (their first column)."
return line.split('\t', 1)[0]
out = MsgpackStreamWriter(output_filename)
out_bad = MsgpackStreamWriter(output_filename + '.reject')
core_prefixes = set()
for line in open(core_filename, encoding='utf-8'):
core_prefixes.add(uri_prefix(line.strip(), 3))
# Scan through the assertions twice to add derived words to the blocklist
blocklist = Blocklist.load(get_support_data_filename(BLOCK_FILENAME))
for iter in range(2):
with open(input_filename, encoding='utf-8') as stream:
for line in stream:
tmp_assertion = _make_assertion([line.strip()])
if tmp_assertion is None:
continue
blocklist.propagate_blocks(tmp_assertion)
with open(input_filename, encoding='utf-8') as stream:
for key, line_group in itertools.groupby(stream, group_func):
assertion = _make_assertion(line_group)
destination = out
if assertion is None:
continue
if assertion['weight'] <= 0:
destination = out_bad
if blocklist.is_blocked(assertion):
destination = out_bad
if assertion['rel'] == 'ExternalURL':
# discard ExternalURL edges for things that aren't otherwise
# in ConceptNet
prefix = uri_prefix(assertion['start'], 3)
if prefix not in core_prefixes:
destination = out_bad
destination.write(assertion)
out.close()
out_bad.close()
def run_opencyc(input_file, output_file):
"""
Read an .nq file containing OpenCyc data, outputting a file of
ConceptNet edges and a file of mappings between the Semantic Web and
ConceptNet.
"""
out = MsgpackStreamWriter(output_file)
labels = {}
unlabels = defaultdict(set)
seen_external_urls = set()
# Read through the file once, finding all the "preferred labels". We will
# use these as the surface texts for the nodes.
for subj, pred, obj, _graph in parse_nquads(open(input_file, encoding='utf-8')):
if pred['url'] == RDF_LABEL:
labels[subj['url']] = obj['text']
unlabels[obj['text']].add(subj['url'])
# Read through the file again and extract ConceptNet edges.
for subj, pred, obj, _graph in parse_nquads(open(input_file, encoding='utf-8')):
rel_name = resource_name(pred['url'])
web_subj = subj.get('url')
web_obj = obj.get('url')
if rel_name == 'subClassOf' and web_obj is not None and web_subj in labels and web_obj in labels:
subj_label = labels[web_subj]
obj_label = labels[web_obj]
if '_' in subj_label or '_' in obj_label:
continue
if subj_label.startswith('xsd:') or obj_label.startswith('xsd:'):
continue
subj_words = set(simple_tokenize(subj_label))
obj_words = set(simple_tokenize(obj_label))
if (subj_words & BLACKLIST_WORDS) or (obj_words & BLACKLIST_WORDS):
continue
if len(subj_words) > 4 or len(obj_words) > 4:
continue
subj_uri = cyc_to_conceptnet_uri(labels, unlabels, web_subj)
obj_uri = cyc_to_conceptnet_uri(labels, unlabels, web_obj)
out.write(opencyc_edge('/r/IsA', subj_uri, obj_uri, subj_label, obj_label))
if (subj_uri, web_subj) not in seen_external_urls:
out.write(external_url_edge(subj_uri, web_subj))
seen_external_urls.add((subj_uri, web_subj))
if (obj_uri, web_obj) not in seen_external_urls:
out.write(external_url_edge(obj_uri, web_obj))
seen_external_urls.add((obj_uri, web_obj))
elif rel_name == 'sameAs' and web_subj in labels and web_obj.startswith('http://umbel.org/'):
subj_label = labels[web_subj]
subj_uri = standardized_concept_uri('en', subj_label)
if (subj_uri, web_obj) not in seen_external_urls:
out.write(external_url_edge(subj_uri, web_obj))
seen_external_urls.add((subj_uri, web_obj))
out.close()
def test_msgpack_to_json():
with TemporaryDirectory(prefix='conceptnet-test') as tmpdir:
json_path = os.path.join(tmpdir, 'test.jsons')
msgpack_path = os.path.join(tmpdir, 'test.msgpack')
writer = MsgpackStreamWriter(json_path)
for item in DATA:
writer.write(item)
writer.close()
msgpack_to_json(json_path, msgpack_path)
reader = read_json_stream(msgpack_path)
for known, read in zip_longest(DATA, reader):
eq_(known, read)
def test_msgpack_to_json():
with TemporaryDirectory(prefix='conceptnet-test') as tmpdir:
json_path = os.path.join(tmpdir, 'test.jsons')
msgpack_path = os.path.join(tmpdir, 'test.msgpack')
writer = MsgpackStreamWriter(json_path)
for item in DATA:
writer.write(item)
writer.close()
msgpack_to_json(json_path, msgpack_path)
reader = read_json_stream(msgpack_path)
for known, read in zip_longest(DATA, reader):
eq_(known, read)
def handle_file(input_file, output_file):
tree = ET.parse(input_file)
out = MsgpackStreamWriter(output_file)
root = tree.getroot()
lang = root[0][1].attrib[
'type'
] # language is at position [1] within the child node [0]
if len(root) >= 2:
for annotation in root[1]:
for word in strip_words(annotation.text):
start = standardized_concept_uri('mul', annotation.attrib['cp'])
end = standardized_concept_uri(lang, word)
edge = make_edge(REL, start, end, DATASET, LICENSE, SOURCE)
out.write(edge)
else:
print("No emoji data in {!r}".format(input_file))
out.close()
def read_wiktionary(input_file, db_file, output_file):
"""
Convert a stream of parsed Wiktionary data into ConceptNet edges.
A `db_file` containing all known words in all languages must have already
been prepared from the same data.
"""
db = sqlite3.connect(db_file)
out = MsgpackStreamWriter(output_file)
for heading, items in segmented_stream(input_file):
language = heading['language']
title = heading['title']
dataset = '/d/wiktionary/{}'.format(language)
url_title = heading['title'].replace(' ', '_')
web_url = 'http://{}.wiktionary.org/wiki/{}'.format(
language, url_title)
web_source = '/s/resource/wiktionary/{}'.format(language)
source = {'contributor': web_source, 'process': PARSER_RULE}
# Scan through the 'from' items, such as the start nodes of
# translations, looking for distinct etymologies. If we get more than
# one etymology for a language, we need to distinguish them as
# different senses in that language.
all_etyms = {
(item['from']['language'], etym_label(language, item['from']))
for item in items
if 'language' in item['from'] and item['from']['text'] == title
and etym_label(language, item['from']) is not None
}
word_languages = {wlang for (wlang, _) in all_etyms}
for wlang in sorted(word_languages):
if valid_language(wlang):
cpage = standardized_concept_uri(wlang, title)
ld_edge = make_edge('/r/ExternalURL',
cpage,
web_url,
dataset=dataset,
weight=0.25,
sources=[source],
license=Licenses.cc_sharealike)
out.write(ld_edge)
etym_to_translation_sense = {}
language_etym_counts = Counter(lang for (lang, etym) in all_etyms)
polysemous_languages = {
lang
for lang in language_etym_counts if language_etym_counts[lang] > 1
}
for item in items:
tfrom = item['from']
tto = item['to']
assumed_languages = [language]
lang1 = tfrom.get('language')
lang2 = tto.get('language')
if lang1 and (lang1
not in assumed_languages) and valid_language(lang1):
assumed_languages.append(lang1)
if lang2 and (lang2
not in assumed_languages) and valid_language(lang2):
assumed_languages.append(lang2)
cfrom = transform_term(language,
tfrom,
assumed_languages,
db,
use_etyms=(lang1 in polysemous_languages))
cpage = cfrom
cto = transform_term(language,
tto,
assumed_languages,
db,
use_etyms=(lang2 in polysemous_languages))
if cfrom is None or cto is None:
continue
if uri_prefix(cfrom, 3) == uri_prefix(cto, 3):
continue
rel, switch = transform_relation(item['rel'])
if rel is None:
continue
if switch:
cfrom, cto = cto, cfrom
# When translations are separated by sense, use only the first
# sense we see for each etymology. That will have the most
# representative translations.
if item['rel'] == 'translation':
etym_key = (tfrom['language'], etym_label(language, tfrom))
sense = tfrom.get('sense', '')
if etym_key in etym_to_translation_sense:
if etym_to_translation_sense[etym_key] != sense:
continue
else:
etym_to_translation_sense[etym_key] = sense
weight = 1.
if rel == '/r/EtymologicallyRelatedTo':
weight = 0.25
edge = make_edge(rel,
cfrom,
cto,
dataset=dataset,
weight=weight,
sources=[source],
surfaceStart=tfrom['text'],
surfaceEnd=tto['text'],
license=Licenses.cc_sharealike)
out.write(edge)
out.close()
def run_wordnet(input_file, output_file):
out = MsgpackStreamWriter(output_file)
synset_senses = defaultdict(list)
sense_synsets = {}
synset_labels = defaultdict(list)
synset_canonical_labels = {}
synset_categories = {}
synset_domains = {}
synset_glosses = {}
synset_disambig = {}
synset_uris = {}
# First pass: find data about synsets
quads = parse_nquads(open(input_file, encoding='utf-8'))
for subj_dict, rel_dict, obj_dict, _graph in quads:
if 'url' not in subj_dict or 'url' not in rel_dict:
continue
subj = subj_dict['url']
rel = rel_dict['url']
obj = obj_dict.get('url')
objtext = obj_dict.get('text')
relname = resource_name(rel)
if relname == 'label':
if obj_dict['lang'] == 'en':
synset_labels[subj].append(objtext)
elif relname == 'sameAs':
if obj.startswith(WN20_URL):
# If we have a link to RDF WordNet 2.0, the URL (URI? IRI?)
# will contain a standardized label for this concept, which
# we should use when we want to use this synset as the name of
# a disambiguation category. RDF WordNet 3.1 assigns synsets
# a number of labels in no particular order, making it hard to
# determine from 3.1 alone what to name a category.
objname = resource_name(obj)
parts = objname.split('-')[1:-2]
# Handle missing apostrophes
label = '-'.join(parts).replace('_s_', "'s_").replace('_s-', "'s_").replace("s__", "s'_").replace("s_-", "s'-").replace('_', ' ')
synset_canonical_labels[subj] = label
elif relname == 'domain_category':
synset_categories[subj] = obj
elif relname == 'lexical_domain':
target = resource_name(obj)
if '.' in target:
domain = target.split('.')[1]
synset_domains[subj] = domain
elif relname == 'gloss':
synset_glosses[subj] = objtext
elif relname == 'reference':
lemma = resource_name(subj)
synset = obj
synset_senses[synset].append(lemma)
sense_synsets[lemma] = synset
used_labels = set(synset_canonical_labels.values())
for synset, values in synset_labels.items():
values.sort(key=lambda label: (label in used_labels,) + label_sort_key(label))
if (
synset not in synset_canonical_labels or
synset_canonical_labels[synset][0].isupper() and synset_domains.get(synset) == 'person'
):
label = values[0]
synset_canonical_labels[synset] = label
used_labels.add(label)
for synset, labels in synset_labels.items():
if synset in synset_categories:
category_name = synset_canonical_labels[synset_categories[synset]]
else:
category_name = synset_domains.get(synset, None)
synset_no_fragment = synset.split('#')[0]
pos = synset_no_fragment[-1].lower()
assert pos in 'nvarsp', synset
if pos == 's':
pos = 'a'
elif pos == 'p':
pos = '-'
if category_name in ('pert', 'all', 'tops'):
category_name = None
synset_disambig[synset] = (pos, category_name)
canon = synset_canonical_labels[synset]
canon_uri = standardized_concept_uri('en', canon, pos, 'wn', category_name)
synset_uris[synset] = canon_uri
for label in labels:
if label != canon:
other_uri = standardized_concept_uri('en', label, pos, 'wn', category_name)
rel_uri = '/r/Synonym'
surface = '[[{0}]] is a synonym of [[{1}]]'.format(label, canon)
edge = make_edge(
rel_uri, other_uri, canon_uri, dataset=DATASET, surfaceText=surface,
license=Licenses.cc_attribution, sources=[SOURCE], weight=2.0
)
out.write(edge)
quads = parse_nquads(open(input_file, encoding='utf-8'))
for subj_dict, rel_dict, obj_dict, _graph in quads:
if 'url' not in subj_dict or 'url' not in rel_dict:
continue
subj = subj_dict['url']
rel = rel_dict['url']
obj = obj_dict.get('url')
relname = resource_name(rel)
if relname in REL_MAPPING:
rel, frame = REL_MAPPING[relname]
reversed_frame = False
if rel.startswith('~'):
rel = rel[1:]
reversed_frame = True
rel_uri = '/r/' + rel
if obj is not None:
obj_uri = synset_uris.get(obj)
if obj not in synset_canonical_labels:
continue
obj_label = synset_canonical_labels[obj]
else:
text = obj_dict['text']
# Some WordNets use strings with "!" in them to indicate
# out-of-band information, such as a missing translation
if (not text) or '!' in text:
continue
lang = obj_dict['lang']
pos, sense = synset_disambig.get(subj, (None, None))
obj_uri = standardized_concept_uri(lang, text, pos, 'wn', sense)
obj_label = text
if subj not in synset_uris or subj not in synset_canonical_labels:
continue
subj_uri = synset_uris[subj]
subj_label = synset_canonical_labels[subj]
license = Licenses.cc_attribution
langcode = subj_uri.split('/')[2]
if langcode in SHAREALIKE_LANGUAGES:
license = Licenses.cc_sharealike
if reversed_frame:
subj_uri, obj_uri = obj_uri, subj_uri
subj_label, obj_label = obj_label, subj_label
surface = frame.format('[[%s]]' % subj_label, '[[%s]]' % obj_label)
edge = make_edge(
rel_uri, subj_uri, obj_uri, dataset=DATASET, surfaceText=surface,
license=license, sources=[SOURCE], weight=2.0
)
out.write(edge)
for wn_url in sorted(synset_uris):
cn_uri = synset_uris[wn_url]
edge = make_edge(
'/r/ExternalURL', cn_uri, wn_url, dataset=DATASET,
license=Licenses.cc_sharealike, sources=[SOURCE], weight=1.0
)
out.write(edge)
out.close()
def json_to_msgpack(input_filename, output_filename):
out_stream = MsgpackStreamWriter(output_filename)
for obj in read_json_stream(input_filename):
out_stream.write(obj)
out_stream.close()
def process_dbpedia(input_dir, output_file, concept_file):
"""
Read through multiple DBPedia files and output filtered assertions to
`output_file`.
"""
ok_concepts = read_concept_file(concept_file)
input_path = pathlib.Path(input_dir)
interlang_path = input_path / 'interlanguage_links_en.tql.bz2'
mapped_urls = interlanguage_mapping(interlang_path, ok_concepts)
out = MsgpackStreamWriter(output_file)
types_path = input_path / 'instance_types_en.tql.bz2'
quads = parse_nquads(bz2.open(str(types_path), 'rt'))
for subj, pred, obj, _graph in quads:
subj_url = subj['url']
if ('Category:' in subj_url or 'File:' in subj_url
or 'List_of' in subj_url or '__' in subj_url
or 'Template:' in subj_url):
continue
if subj_url in mapped_urls:
subj_concept = translate_dbpedia_url(subj_url)
obj_type = un_camel_case(resource_name(obj['url']))
if obj_type not in TYPE_BLACKLIST:
obj_concept = standardized_concept_uri('en', obj_type, 'n')
if obj_concept not in CONCEPT_BLACKLIST:
edge = make_edge('/r/IsA',
subj_concept,
obj_concept,
dataset='/d/dbpedia/en',
license=Licenses.cc_sharealike,
sources=[{
'contributor':
'/s/resource/dbpedia/2015/en'
}],
weight=0.5,
surfaceStart=url_to_label(subj['url']),
surfaceEnd=url_to_label(obj['url']))
out.write(edge)
for other_url in mapped_urls[subj_url]:
if other_url.startswith('http://wikidata.dbpedia.org/'):
urledge = make_edge('/r/ExternalURL',
subj_concept,
other_url,
dataset='/d/dbpedia/en',
license=Licenses.cc_sharealike,
sources=[{
'contributor':
'/s/resource/dbpedia/2015/en'
}],
weight=1.0)
out.write(urledge)
else:
other_concept = translate_dbpedia_url(other_url)
if other_concept:
urledge = make_edge(
'/r/ExternalURL',
other_concept,
other_url,
dataset='/d/dbpedia/en',
license=Licenses.cc_sharealike,
sources=[{
'contributor':
'/s/resource/dbpedia/2015/en'
}],
weight=1.0)
out.write(urledge)
edge = make_edge(
'/r/Synonym',
other_concept,
subj_concept,
dataset='/d/dbpedia/en',
license=Licenses.cc_sharealike,
sources=[{
'contributor':
'/s/resource/dbpedia/2015/en'
}],
weight=0.5,
surfaceStart=url_to_label(other_url),
surfaceEnd=url_to_label(subj_url))
out.write(edge)
relations_path = input_path / 'mappingbased_objects_en.tql.bz2'
quads = parse_nquads(bz2.open(str(relations_path), 'rt'))
for subj, pred, obj, _graph in quads:
subj_concept = translate_dbpedia_url(subj['url'])
obj_concept = translate_dbpedia_url(obj['url'])
rel_name = resource_name(pred['url'])
if (subj_concept and obj_concept and subj['url'] in mapped_urls
and obj['url'] in mapped_urls):
if rel_name in RELATIONS:
rel = RELATIONS[rel_name]
edge = make_edge(rel,
subj_concept,
obj_concept,
dataset='/d/dbpedia/en',
license=Licenses.cc_sharealike,
sources=[{
'contributor':
'/s/resource/dbpedia/2015/en'
}],
weight=0.5,
surfaceStart=url_to_label(subj['url']),
surfaceEnd=url_to_label(obj['url']))
out.write(edge)
out.close()
def run_wordnet(input_file, output_file):
out = MsgpackStreamWriter(output_file)
synset_senses = defaultdict(list)
sense_synsets = {}
synset_labels = defaultdict(list)
synset_canonical_labels = {}
synset_categories = {}
synset_domains = {}
synset_glosses = {}
synset_disambig = {}
synset_uris = {}
# First pass: find data about synsets
quads = parse_nquads(open(input_file, encoding="utf-8"))
for subj_dict, rel_dict, obj_dict, _graph in quads:
if "url" not in subj_dict or "url" not in rel_dict:
continue
subj = subj_dict["url"]
rel = rel_dict["url"]
obj = obj_dict.get("url")
objtext = obj_dict.get("text")
relname = resource_name(rel)
if relname == "label":
if obj_dict["lang"] == "en":
synset_labels[subj].append(objtext)
elif relname == "sameAs":
if obj.startswith(WN20_URL):
# If we have a link to RDF WordNet 2.0, the URL (URI? IRI?)
# will contain a standardized label for this concept, which
# we should use when we want to use this synset as the name of
# a disambiguation category. RDF WordNet 3.1 assigns synsets
# a number of labels in no particular order, making it hard to
# determine from 3.1 alone what to name a category.
objname = resource_name(obj)
parts = objname.split("-")[1:-2]
# Handle missing apostrophes
label = (
"-".join(parts)
.replace("_s_", "'s_")
.replace("_s-", "'s_")
.replace("s__", "s'_")
.replace("s_-", "s'-")
.replace("_", " ")
)
synset_canonical_labels[subj] = label
elif relname == "domain_category":
synset_categories[subj] = obj
elif relname == "lexical_domain":
target = resource_name(obj)
if "." in target:
domain = target.split(".")[1]
synset_domains[subj] = domain
elif relname == "gloss":
synset_glosses[subj] = objtext
elif relname == "reference":
lemma = resource_name(subj)
synset = obj
synset_senses[synset].append(lemma)
sense_synsets[lemma] = synset
used_labels = set(synset_canonical_labels.values())
for synset, values in synset_labels.items():
values.sort(key=lambda label: (label in used_labels,) + label_sort_key(label))
if (
synset not in synset_canonical_labels
or synset_canonical_labels[synset][0].isupper()
and synset_domains.get(synset) == "person"
):
label = values[0]
synset_canonical_labels[synset] = label
used_labels.add(label)
for synset, labels in synset_labels.items():
if synset in synset_categories:
category_name = synset_canonical_labels[synset_categories[synset]]
else:
category_name = synset_domains.get(synset, None)
synset_no_fragment = synset.split("#")[0]
pos = synset_no_fragment[-1].lower()
assert pos in "nvarsp", synset
if pos == "s":
pos = "a"
elif pos == "p":
pos = "-"
if category_name in ("pert", "all", "tops"):
category_name = None
synset_disambig[synset] = (pos, category_name)
canon = synset_canonical_labels[synset]
canon_uri = standardized_concept_uri("en", canon, pos, "wn", category_name)
synset_uris[synset] = canon_uri
for label in labels:
if label != canon:
other_uri = standardized_concept_uri(
"en", label, pos, "wn", category_name
)
rel_uri = "/r/Synonym"
surface = "[[{0}]] is a synonym of [[{1}]]".format(label, canon)
edge = make_edge(
rel_uri,
other_uri,
canon_uri,
dataset=DATASET,
surfaceText=surface,
license=Licenses.cc_attribution,
sources=[SOURCE],
weight=2.0,
)
out.write(edge)
quads = parse_nquads(open(input_file, encoding="utf-8"))
for subj_dict, rel_dict, obj_dict, _graph in quads:
if "url" not in subj_dict or "url" not in rel_dict:
continue
subj = subj_dict["url"]
rel = rel_dict["url"]
obj = obj_dict.get("url")
relname = resource_name(rel)
if relname in REL_MAPPING:
pos, sense = synset_disambig.get(subj, (None, None))
if relname == "hypernym" and pos == "v":
relname = "hypernym-v"
rel, frame = REL_MAPPING[relname]
reversed_frame = False
if rel.startswith("~"):
rel = rel[1:]
reversed_frame = True
rel_uri = "/r/" + rel
if obj is not None:
obj_uri = synset_uris.get(obj)
if obj not in synset_canonical_labels:
continue
obj_label = synset_canonical_labels[obj]
else:
text = obj_dict["text"]
# Some WordNets use strings with "!" in them to indicate
# out-of-band information, such as a missing translation
if (not text) or "!" in text:
continue
lang = obj_dict["lang"]
obj_uri = standardized_concept_uri(lang, text, pos, "wn", sense)
obj_label = text
if subj not in synset_uris or subj not in synset_canonical_labels:
continue
subj_uri = synset_uris[subj]
subj_label = synset_canonical_labels[subj]
license = Licenses.cc_attribution
langcode = subj_uri.split("/")[2]
if langcode in SHAREALIKE_LANGUAGES:
license = Licenses.cc_sharealike
if reversed_frame:
subj_uri, obj_uri = obj_uri, subj_uri
subj_label, obj_label = obj_label, subj_label
surface = frame.format("[[%s]]" % subj_label, "[[%s]]" % obj_label)
edge = make_edge(
rel_uri,
subj_uri,
obj_uri,
dataset=DATASET,
surfaceText=surface,
license=license,
sources=[SOURCE],
weight=2.0,
)
out.write(edge)
for wn_url in sorted(synset_uris):
cn_uri = synset_uris[wn_url]
edge = make_edge(
"/r/ExternalURL",
cn_uri,
wn_url,
dataset=DATASET,
license=Licenses.cc_sharealike,
sources=[SOURCE],
weight=1.0,
)
out.write(edge)
out.close()
def run_wordnet(input_file, output_file):
out = MsgpackStreamWriter(output_file)
synset_senses = defaultdict(list)
sense_synsets = {}
synset_labels = defaultdict(list)
synset_canonical_labels = {}
synset_categories = {}
synset_domains = {}
synset_glosses = {}
synset_disambig = {}
synset_uris = {}
# First pass: find data about synsets
quads = parse_nquads(open(input_file, encoding='utf-8'))
for subj_dict, rel_dict, obj_dict, _graph in quads:
if 'url' not in subj_dict or 'url' not in rel_dict:
continue
subj = subj_dict['url']
rel = rel_dict['url']
obj = obj_dict.get('url')
objtext = obj_dict.get('text')
relname = resource_name(rel)
if relname == 'label':
if obj_dict['lang'] == 'en':
synset_labels[subj].append(objtext)
elif relname == 'sameAs':
if obj.startswith(WN20_URL):
# If we have a link to RDF WordNet 2.0, the URL (URI? IRI?)
# will contain a standardized label for this concept, which
# we should use when we want to use this synset as the name of
# a disambiguation category. RDF WordNet 3.1 assigns synsets
# a number of labels in no particular order, making it hard to
# determine from 3.1 alone what to name a category.
objname = resource_name(obj)
parts = objname.split('-')[1:-2]
# Handle missing apostrophes
label = '-'.join(parts).replace('_s_', "'s_").replace(
'_s-',
"'s_").replace("s__",
"s'_").replace("s_-",
"s'-").replace('_', ' ')
synset_canonical_labels[subj] = label
elif relname == 'domain_category':
synset_categories[subj] = obj
elif relname == 'lexical_domain':
target = resource_name(obj)
if '.' in target:
domain = target.split('.')[1]
synset_domains[subj] = domain
elif relname == 'gloss':
synset_glosses[subj] = objtext
elif relname == 'reference':
lemma = resource_name(subj)
synset = obj
synset_senses[synset].append(lemma)
sense_synsets[lemma] = synset
used_labels = set(synset_canonical_labels.values())
for synset, values in synset_labels.items():
values.sort(
key=lambda label: (label in used_labels, ) + label_sort_key(label))
if (synset not in synset_canonical_labels
or synset_canonical_labels[synset][0].isupper()
and synset_domains.get(synset) == 'person'):
label = values[0]
synset_canonical_labels[synset] = label
used_labels.add(label)
for synset, labels in synset_labels.items():
if synset in synset_categories:
category_name = synset_canonical_labels[synset_categories[synset]]
else:
category_name = synset_domains.get(synset, None)
synset_no_fragment = synset.split('#')[0]
pos = synset_no_fragment[-1].lower()
assert pos in 'nvarsp', synset
if pos == 's':
pos = 'a'
elif pos == 'p':
pos = '-'
if category_name in ('pert', 'all', 'tops'):
category_name = None
synset_disambig[synset] = (pos, category_name)
canon = synset_canonical_labels[synset]
canon_uri = standardized_concept_uri('en', canon, pos, 'wn',
category_name)
synset_uris[synset] = canon_uri
for label in labels:
if label != canon:
other_uri = standardized_concept_uri('en', label, pos, 'wn',
category_name)
rel_uri = '/r/Synonym'
surface = '[[{0}]] is a synonym of [[{1}]]'.format(
label, canon)
edge = make_edge(rel_uri,
other_uri,
canon_uri,
dataset=DATASET,
surfaceText=surface,
license=Licenses.cc_attribution,
sources=[SOURCE],
weight=2.0)
out.write(edge)
quads = parse_nquads(open(input_file, encoding='utf-8'))
for subj_dict, rel_dict, obj_dict, _graph in quads:
if 'url' not in subj_dict or 'url' not in rel_dict:
continue
subj = subj_dict['url']
rel = rel_dict['url']
obj = obj_dict.get('url')
relname = resource_name(rel)
if relname in REL_MAPPING:
pos, sense = synset_disambig.get(subj, (None, None))
if relname == 'hypernym' and pos == 'v':
relname = 'hypernym-v'
rel, frame = REL_MAPPING[relname]
reversed_frame = False
if rel.startswith('~'):
rel = rel[1:]
reversed_frame = True
rel_uri = '/r/' + rel
if obj is not None:
obj_uri = synset_uris.get(obj)
if obj not in synset_canonical_labels:
continue
obj_label = synset_canonical_labels[obj]
else:
text = obj_dict['text']
# Some WordNets use strings with "!" in them to indicate
# out-of-band information, such as a missing translation
if (not text) or '!' in text:
continue
lang = obj_dict['lang']
obj_uri = standardized_concept_uri(lang, text, pos, 'wn',
sense)
obj_label = text
if subj not in synset_uris or subj not in synset_canonical_labels:
continue
subj_uri = synset_uris[subj]
subj_label = synset_canonical_labels[subj]
license = Licenses.cc_attribution
langcode = subj_uri.split('/')[2]
if langcode in SHAREALIKE_LANGUAGES:
license = Licenses.cc_sharealike
if reversed_frame:
subj_uri, obj_uri = obj_uri, subj_uri
subj_label, obj_label = obj_label, subj_label
surface = frame.format('[[%s]]' % subj_label, '[[%s]]' % obj_label)
edge = make_edge(rel_uri,
subj_uri,
obj_uri,
dataset=DATASET,
surfaceText=surface,
license=license,
sources=[SOURCE],
weight=2.0)
out.write(edge)
for wn_url in sorted(synset_uris):
cn_uri = synset_uris[wn_url]
edge = make_edge('/r/ExternalURL',
cn_uri,
wn_url,
dataset=DATASET,
license=Licenses.cc_sharealike,
sources=[SOURCE],
weight=1.0)
out.write(edge)
out.close()
def json_to_msgpack(input_filename, output_filename): out_stream = MsgpackStreamWriter(output_filename) for obj in read_json_stream(input_filename): out_stream.write(obj) out_stream.close()
def process_dbpedia(input_dir, output_file, concept_file):
"""
Read through multiple DBPedia files and output filtered assertions to
`output_file`.
"""
ok_concepts = read_concept_file(concept_file)
input_path = pathlib.Path(input_dir)
interlang_path = input_path / 'interlanguage_links_en.tql.bz2'
mapped_urls = interlanguage_mapping(interlang_path, ok_concepts)
out = MsgpackStreamWriter(output_file)
types_path = input_path / 'instance_types_en.tql.bz2'
quads = parse_nquads(bz2.open(str(types_path), 'rt'))
for subj, pred, obj, _graph in quads:
subj_url = subj['url']
if (
'Category:' in subj_url or 'File:' in subj_url or
'List_of' in subj_url or '__' in subj_url or
'Template:' in subj_url
):
continue
if subj_url in mapped_urls:
subj_concept = translate_dbpedia_url(subj_url)
obj_type = un_camel_case(resource_name(obj['url']))
if obj_type not in TYPE_BLACKLIST:
obj_concept = standardized_concept_uri('en', obj_type, 'n')
if obj_concept not in CONCEPT_BLACKLIST:
edge = make_edge(
'/r/IsA', subj_concept, obj_concept,
dataset='/d/dbpedia/en',
license=Licenses.cc_sharealike,
sources=[{'contributor': '/s/resource/dbpedia/2015/en'}],
weight=0.5,
surfaceStart=url_to_label(subj['url']),
surfaceEnd=url_to_label(obj['url'])
)
out.write(edge)
for other_url in mapped_urls[subj_url]:
if other_url.startswith('http://wikidata.dbpedia.org/'):
urledge = make_edge(
'/r/ExternalURL',
subj_concept, other_url,
dataset='/d/dbpedia/en',
license=Licenses.cc_sharealike,
sources=[{'contributor': '/s/resource/dbpedia/2015/en'}],
weight=1.0
)
out.write(urledge)
else:
other_concept = translate_dbpedia_url(other_url)
if other_concept:
urledge = make_edge(
'/r/ExternalURL',
other_concept, other_url,
dataset='/d/dbpedia/en',
license=Licenses.cc_sharealike,
sources=[{'contributor': '/s/resource/dbpedia/2015/en'}],
weight=1.0
)
out.write(urledge)
edge = make_edge(
'/r/Synonym',
other_concept, subj_concept,
dataset='/d/dbpedia/en',
license=Licenses.cc_sharealike,
sources=[{'contributor': '/s/resource/dbpedia/2015/en'}],
weight=0.5,
surfaceStart=url_to_label(other_url),
surfaceEnd=url_to_label(subj_url)
)
out.write(edge)
relations_path = input_path / 'mappingbased_objects_en.tql.bz2'
quads = parse_nquads(bz2.open(str(relations_path), 'rt'))
for subj, pred, obj, _graph in quads:
subj_concept = translate_dbpedia_url(subj['url'])
obj_concept = translate_dbpedia_url(obj['url'])
rel_name = resource_name(pred['url'])
if (
subj_concept and obj_concept and
subj['url'] in mapped_urls and obj['url'] in mapped_urls
):
if rel_name in RELATIONS:
rel = RELATIONS[rel_name]
edge = make_edge(
rel, subj_concept, obj_concept,
dataset='/d/dbpedia/en',
license=Licenses.cc_sharealike,
sources=[{'contributor': '/s/resource/dbpedia/2015/en'}],
weight=0.5,
surfaceStart=url_to_label(subj['url']),
surfaceEnd=url_to_label(obj['url'])
)
out.write(edge)
out.close()
def read_wiktionary(input_file, db_file, output_file):
"""
Convert a stream of parsed Wiktionary data into ConceptNet edges.
A `db_file` containing all known words in all languages must have already
been prepared from the same data.
"""
db = sqlite3.connect(db_file)
out = MsgpackStreamWriter(output_file)
for heading, items in segmented_stream(input_file):
language = heading['language']
title = heading['title']
dataset = '/d/wiktionary/{}'.format(language)
url_title = heading['title'].replace(' ', '_')
web_url = 'http://{}.wiktionary.org/wiki/{}'.format(language, url_title)
web_source = '/s/resource/wiktionary/{}'.format(language)
source = {
'contributor': web_source,
'process': PARSER_RULE
}
# Scan through the 'from' items, such as the start nodes of
# translations, looking for distinct etymologies. If we get more than
# one etymology for a language, we need to distinguish them as
# different senses in that language.
all_etyms = {
(item['from']['language'], etym_label(language, item['from']))
for item in items
if 'language' in item['from'] and item['from']['text'] == title
and etym_label(language, item['from']) is not None
}
word_languages = {wlang for (wlang, _) in all_etyms}
for wlang in sorted(word_languages):
cpage = standardized_concept_uri(wlang, title)
ld_edge = make_edge(
'/r/ExternalURL', cpage, web_url,
dataset=dataset, weight=0.25, sources=[source],
license=Licenses.cc_sharealike
)
out.write(ld_edge)
etym_to_translation_sense = {}
language_etym_counts = Counter(lang for (lang, etym) in all_etyms)
polysemous_languages = {
lang for lang in language_etym_counts
if language_etym_counts[lang] > 1
}
for item in items:
tfrom = item['from']
tto = item['to']
assumed_languages = [language]
lang1 = tfrom.get('language')
lang2 = tto.get('language')
if lang1 and (lang1 not in assumed_languages) and valid_language(lang1):
assumed_languages.append(lang1)
if lang2 and (lang2 not in assumed_languages) and valid_language(lang2):
assumed_languages.append(lang2)
cfrom = transform_term(
language, tfrom, assumed_languages, db,
use_etyms=(lang1 in polysemous_languages)
)
cpage = cfrom
cto = transform_term(
language, tto, assumed_languages, db,
use_etyms=(lang2 in polysemous_languages)
)
if cfrom is None or cto is None:
continue
if uri_prefix(cfrom, 3) == uri_prefix(cto, 3):
continue
rel, switch = transform_relation(item['rel'])
if rel is None:
continue
if switch:
cfrom, cto = cto, cfrom
# When translations are separated by sense, use only the first
# sense we see for each etymology. That will have the most
# representative translations.
if item['rel'] == 'translation':
etym_key = (tfrom['language'], etym_label(language, tfrom))
sense = tfrom.get('sense', '')
if etym_key in etym_to_translation_sense:
if etym_to_translation_sense[etym_key] != sense:
continue
else:
etym_to_translation_sense[etym_key] = sense
weight = 1.
if rel == '/r/EtymologicallyRelatedTo':
weight = 0.25
edge = make_edge(rel, cfrom, cto, dataset=dataset, weight=weight,
sources=[source],
surfaceStart=tfrom['text'],
surfaceEnd=tto['text'],
license=Licenses.cc_sharealike)
out.write(edge)
out.close()
