spaCy/bin/wiki_entity_linking/wikidata_processor.py

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# coding: utf-8
from __future__ import unicode_literals
import gzip
import json
import logging
import datetime
logger = logging.getLogger(__name__)
def read_wikidata_entities_json(wikidata_file, limit=None, to_print=False, lang="en", parse_descriptions=True):
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# Read the JSON wiki data and parse out the entities. Takes about 7u30 to parse 55M lines.
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# get latest-all.json.bz2 from https://dumps.wikimedia.org/wikidatawiki/entities/
site_filter = '{}wiki'.format(lang)
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# properties filter (currently disabled to get ALL data)
prop_filter = dict()
# prop_filter = {'P31': {'Q5', 'Q15632617'}} # currently defined as OR: one property suffices to be selected
title_to_id = dict()
id_to_descr = dict()
# parse appropriate fields - depending on what we need in the KB
parse_properties = False
parse_sitelinks = True
parse_labels = False
parse_aliases = False
parse_claims = False
with gzip.open(wikidata_file, mode='rb') as file:
for cnt, line in enumerate(file):
if limit and cnt >= limit:
break
if cnt % 500000 == 0:
logger.info("processed {} lines of WikiData dump".format(cnt))
clean_line = line.strip()
if clean_line.endswith(b","):
clean_line = clean_line[:-1]
if len(clean_line) > 1:
obj = json.loads(clean_line)
entry_type = obj["type"]
if entry_type == "item":
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# filtering records on their properties (currently disabled to get ALL data)
# keep = False
keep = True
claims = obj["claims"]
if parse_claims:
for prop, value_set in prop_filter.items():
claim_property = claims.get(prop, None)
if claim_property:
for cp in claim_property:
CLI scripts for entity linking (wikipedia & generic) (#4091) * document token ent_kb_id * document span kb_id * update pipeline documentation * prior and context weights as bool's instead * entitylinker api documentation * drop for both models * finish entitylinker documentation * small fixes * documentation for KB * candidate documentation * links to api pages in code * small fix * frequency examples as counts for consistency * consistent documentation about tensors returned by predict * add entity linking to usage 101 * add entity linking infobox and KB section to 101 * entity-linking in linguistic features * small typo corrections * training example and docs for entity_linker * predefined nlp and kb * revert back to similarity encodings for simplicity (for now) * set prior probabilities to 0 when excluded * code clean up * bugfix: deleting kb ID from tokens when entities were removed * refactor train el example to use either model or vocab * pretrain_kb example for example kb generation * add to training docs for KB + EL example scripts * small fixes * error numbering * ensure the language of vocab and nlp stay consistent across serialization * equality with = * avoid conflict in errors file * add error 151 * final adjustements to the train scripts - consistency * update of goldparse documentation * small corrections * push commit * turn kb_creator into CLI script (wip) * proper parameters for training entity vectors * wikidata pipeline split up into two executable scripts * remove context_width * move wikidata scripts in bin directory, remove old dummy script * refine KB script with logs and preprocessing options * small edits * small improvements to logging of EL CLI script
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cp_id = (
cp["mainsnak"]
.get("datavalue", {})
.get("value", {})
.get("id")
)
cp_rank = cp["rank"]
if cp_rank != "deprecated" and cp_id in value_set:
keep = True
if keep:
unique_id = obj["id"]
if to_print:
print("ID:", unique_id)
print("type:", entry_type)
# parsing all properties that refer to other entities
if parse_properties:
for prop, claim_property in claims.items():
CLI scripts for entity linking (wikipedia & generic) (#4091) * document token ent_kb_id * document span kb_id * update pipeline documentation * prior and context weights as bool's instead * entitylinker api documentation * drop for both models * finish entitylinker documentation * small fixes * documentation for KB * candidate documentation * links to api pages in code * small fix * frequency examples as counts for consistency * consistent documentation about tensors returned by predict * add entity linking to usage 101 * add entity linking infobox and KB section to 101 * entity-linking in linguistic features * small typo corrections * training example and docs for entity_linker * predefined nlp and kb * revert back to similarity encodings for simplicity (for now) * set prior probabilities to 0 when excluded * code clean up * bugfix: deleting kb ID from tokens when entities were removed * refactor train el example to use either model or vocab * pretrain_kb example for example kb generation * add to training docs for KB + EL example scripts * small fixes * error numbering * ensure the language of vocab and nlp stay consistent across serialization * equality with = * avoid conflict in errors file * add error 151 * final adjustements to the train scripts - consistency * update of goldparse documentation * small corrections * push commit * turn kb_creator into CLI script (wip) * proper parameters for training entity vectors * wikidata pipeline split up into two executable scripts * remove context_width * move wikidata scripts in bin directory, remove old dummy script * refine KB script with logs and preprocessing options * small edits * small improvements to logging of EL CLI script
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cp_dicts = [
cp["mainsnak"]["datavalue"].get("value")
for cp in claim_property
if cp["mainsnak"].get("datavalue")
]
cp_values = [
cp_dict.get("id")
for cp_dict in cp_dicts
if isinstance(cp_dict, dict)
if cp_dict.get("id") is not None
]
if cp_values:
if to_print:
print("prop:", prop, cp_values)
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found_link = False
if parse_sitelinks:
site_value = obj["sitelinks"].get(site_filter, None)
if site_value:
CLI scripts for entity linking (wikipedia & generic) (#4091) * document token ent_kb_id * document span kb_id * update pipeline documentation * prior and context weights as bool's instead * entitylinker api documentation * drop for both models * finish entitylinker documentation * small fixes * documentation for KB * candidate documentation * links to api pages in code * small fix * frequency examples as counts for consistency * consistent documentation about tensors returned by predict * add entity linking to usage 101 * add entity linking infobox and KB section to 101 * entity-linking in linguistic features * small typo corrections * training example and docs for entity_linker * predefined nlp and kb * revert back to similarity encodings for simplicity (for now) * set prior probabilities to 0 when excluded * code clean up * bugfix: deleting kb ID from tokens when entities were removed * refactor train el example to use either model or vocab * pretrain_kb example for example kb generation * add to training docs for KB + EL example scripts * small fixes * error numbering * ensure the language of vocab and nlp stay consistent across serialization * equality with = * avoid conflict in errors file * add error 151 * final adjustements to the train scripts - consistency * update of goldparse documentation * small corrections * push commit * turn kb_creator into CLI script (wip) * proper parameters for training entity vectors * wikidata pipeline split up into two executable scripts * remove context_width * move wikidata scripts in bin directory, remove old dummy script * refine KB script with logs and preprocessing options * small edits * small improvements to logging of EL CLI script
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site = site_value["title"]
if to_print:
print(site_filter, ":", site)
title_to_id[site] = unique_id
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found_link = True
if parse_labels:
labels = obj["labels"]
if labels:
lang_label = labels.get(lang, None)
if lang_label:
if to_print:
CLI scripts for entity linking (wikipedia & generic) (#4091) * document token ent_kb_id * document span kb_id * update pipeline documentation * prior and context weights as bool's instead * entitylinker api documentation * drop for both models * finish entitylinker documentation * small fixes * documentation for KB * candidate documentation * links to api pages in code * small fix * frequency examples as counts for consistency * consistent documentation about tensors returned by predict * add entity linking to usage 101 * add entity linking infobox and KB section to 101 * entity-linking in linguistic features * small typo corrections * training example and docs for entity_linker * predefined nlp and kb * revert back to similarity encodings for simplicity (for now) * set prior probabilities to 0 when excluded * code clean up * bugfix: deleting kb ID from tokens when entities were removed * refactor train el example to use either model or vocab * pretrain_kb example for example kb generation * add to training docs for KB + EL example scripts * small fixes * error numbering * ensure the language of vocab and nlp stay consistent across serialization * equality with = * avoid conflict in errors file * add error 151 * final adjustements to the train scripts - consistency * update of goldparse documentation * small corrections * push commit * turn kb_creator into CLI script (wip) * proper parameters for training entity vectors * wikidata pipeline split up into two executable scripts * remove context_width * move wikidata scripts in bin directory, remove old dummy script * refine KB script with logs and preprocessing options * small edits * small improvements to logging of EL CLI script
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print(
"label (" + lang + "):", lang_label["value"]
)
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if found_link and parse_descriptions:
descriptions = obj["descriptions"]
if descriptions:
lang_descr = descriptions.get(lang, None)
if lang_descr:
if to_print:
CLI scripts for entity linking (wikipedia & generic) (#4091) * document token ent_kb_id * document span kb_id * update pipeline documentation * prior and context weights as bool's instead * entitylinker api documentation * drop for both models * finish entitylinker documentation * small fixes * documentation for KB * candidate documentation * links to api pages in code * small fix * frequency examples as counts for consistency * consistent documentation about tensors returned by predict * add entity linking to usage 101 * add entity linking infobox and KB section to 101 * entity-linking in linguistic features * small typo corrections * training example and docs for entity_linker * predefined nlp and kb * revert back to similarity encodings for simplicity (for now) * set prior probabilities to 0 when excluded * code clean up * bugfix: deleting kb ID from tokens when entities were removed * refactor train el example to use either model or vocab * pretrain_kb example for example kb generation * add to training docs for KB + EL example scripts * small fixes * error numbering * ensure the language of vocab and nlp stay consistent across serialization * equality with = * avoid conflict in errors file * add error 151 * final adjustements to the train scripts - consistency * update of goldparse documentation * small corrections * push commit * turn kb_creator into CLI script (wip) * proper parameters for training entity vectors * wikidata pipeline split up into two executable scripts * remove context_width * move wikidata scripts in bin directory, remove old dummy script * refine KB script with logs and preprocessing options * small edits * small improvements to logging of EL CLI script
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print(
"description (" + lang + "):",
lang_descr["value"],
)
id_to_descr[unique_id] = lang_descr["value"]
if parse_aliases:
aliases = obj["aliases"]
if aliases:
lang_aliases = aliases.get(lang, None)
if lang_aliases:
for item in lang_aliases:
if to_print:
CLI scripts for entity linking (wikipedia & generic) (#4091) * document token ent_kb_id * document span kb_id * update pipeline documentation * prior and context weights as bool's instead * entitylinker api documentation * drop for both models * finish entitylinker documentation * small fixes * documentation for KB * candidate documentation * links to api pages in code * small fix * frequency examples as counts for consistency * consistent documentation about tensors returned by predict * add entity linking to usage 101 * add entity linking infobox and KB section to 101 * entity-linking in linguistic features * small typo corrections * training example and docs for entity_linker * predefined nlp and kb * revert back to similarity encodings for simplicity (for now) * set prior probabilities to 0 when excluded * code clean up * bugfix: deleting kb ID from tokens when entities were removed * refactor train el example to use either model or vocab * pretrain_kb example for example kb generation * add to training docs for KB + EL example scripts * small fixes * error numbering * ensure the language of vocab and nlp stay consistent across serialization * equality with = * avoid conflict in errors file * add error 151 * final adjustements to the train scripts - consistency * update of goldparse documentation * small corrections * push commit * turn kb_creator into CLI script (wip) * proper parameters for training entity vectors * wikidata pipeline split up into two executable scripts * remove context_width * move wikidata scripts in bin directory, remove old dummy script * refine KB script with logs and preprocessing options * small edits * small improvements to logging of EL CLI script
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print(
"alias (" + lang + "):", item["value"]
)
if to_print:
print()
return title_to_id, id_to_descr
def write_entity_files(entity_def_output, title_to_id):
with entity_def_output.open("w", encoding="utf8") as id_file:
id_file.write("WP_title" + "|" + "WD_id" + "\n")
for title, qid in title_to_id.items():
id_file.write(title + "|" + str(qid) + "\n")
def write_entity_description_files(entity_descr_output, id_to_descr):
with entity_descr_output.open("w", encoding="utf8") as descr_file:
descr_file.write("WD_id" + "|" + "description" + "\n")
for qid, descr in id_to_descr.items():
descr_file.write(str(qid) + "|" + descr + "\n")