# coding: utf-8 """Script to process Wikipedia and Wikidata dumps and create a knowledge base (KB) with specific parameters. Intermediate files are written to disk. Running the full pipeline on a standard laptop, may take up to 13 hours of processing. Use the -p, -d and -s options to speed up processing using the intermediate files from a previous run. For the Wikidata dump: get the latest-all.json.bz2 from https://dumps.wikimedia.org/wikidatawiki/entities/ For the Wikipedia dump: get enwiki-latest-pages-articles-multistream.xml.bz2 from https://dumps.wikimedia.org/enwiki/latest/ """ from __future__ import unicode_literals import logging from pathlib import Path import plac from bin.wiki_entity_linking import wikipedia_processor as wp, wikidata_processor as wd from bin.wiki_entity_linking import kb_creator from bin.wiki_entity_linking import training_set_creator from bin.wiki_entity_linking import TRAINING_DATA_FILE, KB_FILE, ENTITY_DESCR_PATH, KB_MODEL_DIR, LOG_FORMAT from bin.wiki_entity_linking import ENTITY_FREQ_PATH, PRIOR_PROB_PATH, ENTITY_DEFS_PATH import spacy logger = logging.getLogger(__name__) @plac.annotations( wd_json=("Path to the downloaded WikiData JSON dump.", "positional", None, Path), wp_xml=("Path to the downloaded Wikipedia XML dump.", "positional", None, Path), output_dir=("Output directory", "positional", None, Path), model=("Model name or path, should include pretrained vectors.", "positional", None, str), max_per_alias=("Max. # entities per alias (default 10)", "option", "a", int), min_freq=("Min. count of an entity in the corpus (default 20)", "option", "f", int), min_pair=("Min. count of entity-alias pairs (default 5)", "option", "c", int), entity_vector_length=("Length of entity vectors (default 64)", "option", "v", int), loc_prior_prob=("Location to file with prior probabilities", "option", "p", Path), loc_entity_defs=("Location to file with entity definitions", "option", "d", Path), loc_entity_desc=("Location to file with entity descriptions", "option", "s", Path), descriptions_from_wikipedia=("Flag for using wp descriptions not wd", "flag", "wp"), limit=("Optional threshold to limit lines read from dumps", "option", "l", int), lang=("Optional language for which to get wikidata titles. Defaults to 'en'", "option", "la", str), ) def main( wd_json, wp_xml, output_dir, model, max_per_alias=10, min_freq=20, min_pair=5, entity_vector_length=64, loc_prior_prob=None, loc_entity_defs=None, loc_entity_desc=None, descriptions_from_wikipedia=False, limit=None, lang="en", ): entity_defs_path = loc_entity_defs if loc_entity_defs else output_dir / ENTITY_DEFS_PATH entity_descr_path = loc_entity_desc if loc_entity_desc else output_dir / ENTITY_DESCR_PATH entity_freq_path = output_dir / ENTITY_FREQ_PATH prior_prob_path = loc_prior_prob if loc_prior_prob else output_dir / PRIOR_PROB_PATH training_entities_path = output_dir / TRAINING_DATA_FILE kb_path = output_dir / KB_FILE logger.info("Creating KB with Wikipedia and WikiData") if limit is not None: logger.warning("Warning: reading only {} lines of Wikipedia/Wikidata dumps.".format(limit)) # STEP 0: set up IO if not output_dir.exists(): output_dir.mkdir(parents=True) # STEP 1: create the NLP object logger.info("STEP 1: Loading model {}".format(model)) nlp = spacy.load(model) # check the length of the nlp vectors if "vectors" not in nlp.meta or not nlp.vocab.vectors.size: raise ValueError( "The `nlp` object should have access to pre-trained word vectors, " " cf. https://spacy.io/usage/models#languages." ) # STEP 2: create prior probabilities from WP if not prior_prob_path.exists(): # It takes about 2h to process 1000M lines of Wikipedia XML dump logger.info("STEP 2: writing prior probabilities to {}".format(prior_prob_path)) wp.read_prior_probs(wp_xml, prior_prob_path, limit=limit) logger.info("STEP 2: reading prior probabilities from {}".format(prior_prob_path)) # STEP 3: deduce entity frequencies from WP (takes only a few minutes) logger.info("STEP 3: calculating entity frequencies") wp.write_entity_counts(prior_prob_path, entity_freq_path, to_print=False) # STEP 4: reading definitions and (possibly) descriptions from WikiData or from file message = " and descriptions" if not descriptions_from_wikipedia else "" if (not entity_defs_path.exists()) or (not descriptions_from_wikipedia and not entity_descr_path.exists()): # It takes about 10h to process 55M lines of Wikidata JSON dump logger.info("STEP 4: parsing wikidata for entity definitions" + message) title_to_id, id_to_descr = wd.read_wikidata_entities_json( wd_json, limit, to_print=False, lang=lang, parse_descriptions=(not descriptions_from_wikipedia), ) wd.write_entity_files(entity_defs_path, title_to_id) if not descriptions_from_wikipedia: wd.write_entity_description_files(entity_descr_path, id_to_descr) logger.info("STEP 4: read entity definitions" + message) # STEP 5: Getting gold entities from wikipedia message = " and descriptions" if descriptions_from_wikipedia else "" if (not training_entities_path.exists()) or (descriptions_from_wikipedia and not entity_descr_path.exists()): logger.info("STEP 5: parsing wikipedia for gold entities" + message) training_set_creator.create_training_examples_and_descriptions( wp_xml, entity_defs_path, entity_descr_path, training_entities_path, parse_descriptions=descriptions_from_wikipedia, limit=limit, ) logger.info("STEP 5: read gold entities" + message) # STEP 6: creating the actual KB # It takes ca. 30 minutes to pretrain the entity embeddings logger.info("STEP 6: creating the KB at {}".format(kb_path)) kb = kb_creator.create_kb( nlp=nlp, max_entities_per_alias=max_per_alias, min_entity_freq=min_freq, min_occ=min_pair, entity_def_input=entity_defs_path, entity_descr_path=entity_descr_path, count_input=entity_freq_path, prior_prob_input=prior_prob_path, entity_vector_length=entity_vector_length, ) kb.dump(kb_path) nlp.to_disk(output_dir / KB_MODEL_DIR) logger.info("Done!") if __name__ == "__main__": logging.basicConfig(level=logging.INFO, format=LOG_FORMAT) plac.call(main)