def __entity_centric_mapper(self, entity_id):
"""Gets the list of DBpedia types for a given entityID."""
en = Entity()
all_types = en.lookup_en(entity_id).get(RDF_TYPE_PROP, [])
final_types = [t for t in all_types
if self.__valid_final_ec_type(t)] # filtering
return final_types
def main(args):
# config = {Entity.COLLECTION: args.collection}
# if args.lookup_entity:
# config[Entity.OPERATION] = Entity.LOOKUP_ENTITY
# config[Entity.PARAMETERS] = {Entity.ID: args.id}
en = Entity()
if args.operation == "lookup_entity":
res = en.lookup_en(args.input)
elif args.operation == "lookup_name":
res = en.lookup_name_dbpedia(args.input)
# res = en.lookup_name_facc(args.input)
pprint(res)
def gen_train_set(gt, query_file, train_set):
"""Trains LTR model for entity linking."""
entity, elastic, fcache = Entity(), ElasticCache(
ELASTIC_INDICES[0]), FeatureCache()
inss = Instances()
positive_annots = set()
# Adds groundtruth instances (positive instances)
PLOGGER.info("Adding groundtruth instances (positive instances) ....")
for item in sorted(gt): # qid, query, en_id, mention
ltr = LTR(Query(item[1], item[0]), entity, elastic, fcache)
ins = ltr.__gen_raw_ins(item[2], item[3])
ins.features = ltr.get_features(ins)
ins.target = 1
inss.add_instance(ins)
positive_annots.add((item[0], item[2]))
# Adds all other instances
PLOGGER.info("Adding all other instances (negative instances) ...")
for qid, q in sorted(json.load(open(query_file, "r")).items()):
PLOGGER.info("Query [" + qid + "]")
ltr = LTR(Query(q, qid), entity, elastic, fcache)
q_inss = ltr.get_candidate_inss()
for ins in q_inss.get_all():
if (qid, ins.get_property("en_id")) in positive_annots:
continue
ins.target = 0
inss.add_instance(ins)
inss.to_json(train_set)
def main(args):
config = FileUtils.load_config(args.config)
el = EL(config, Entity())
if args.query:
res = el.link(args.query)
pprint(res)
else:
el.batch_linking()
def main(args):
conf = FileUtils.load_config(args.config)
el = EL(conf, Entity(), ElasticCache(DBPEDIA_INDEX), FeatureCache())
if conf.get("gen_model", False):
LTR.train(conf)
elif args.query:
res = el.link(args.query)
pprint(res)
else:
el.batch_linking()
def main(args):
en = Entity()
if args.operation == "lookup_id":
res = en.lookup_en(args.input)
elif args.operation == "lookup_sf_dbpedia":
res = en.lookup_name_dbpedia(args.input)
elif args.operation == "lookup_sf_facc":
res = en.lookup_name_facc(args.input)
elif args.operation == "freebase2dbpedia":
res = en.fb_to_dbp(args.input)
elif args.operation == "dbpedia2freebase":
res = en.dbp_to_fb(args.input)
pprint(res)
from nordlys.core.retrieval.elastic_cache import ElasticCache
from nordlys.logic.entity.entity import Entity
from nordlys.logic.features.feature_cache import FeatureCache
from nordlys.services.el import EL
from nordlys.services.er import ER
from nordlys.services.tti import TTI
from nordlys.core.utils.logging_utils import RequestHandler
from nordlys.core.utils.Api_handler import API_Handler
import logging, traceback
from time import strftime
from nordlys.config import LOGGING_PATH, PLOGGER, ELASTIC_INDICES, Api_Log_Path
# Variables
DBPEDIA_INDEX = ELASTIC_INDICES[0]
__entity = Entity()
__elastic = ElasticCache(DBPEDIA_INDEX)
__fcache = FeatureCache()
app = Flask(__name__)
def error(str):
"""
@todo complete error handling
:param str:
:return:
"""
res = {"ERROR": str}
return jsonify(**res)
def main(args):
entity = Entity()
query = Query(args[0])
cmns = Cmns(query, entity, cmns_th=0.1)
print(cmns.link())
def main(args):
entity = Entity()
mention = Mention(args[0], entity, cmns_th=0.1)
ens = mention.get_cand_ens()
print(ens)
def main():
sf = Entity(MONGO_COLLECTION_SF_FACC)
men_en = FtrMentionEntity(sf, sources=['facc12'])
print(men_en.commonness("/m/0ngyw16", "009 re:cyborg"))