Python Mongo_Connector Exemples

Exemple #1

Fichier : EL.py Projet : svakulenk0/KBQA

.. codeauthor: svitlana vakulenko
    <*****@*****.**>

Evaluate entity linking performance and store annotations
'''

# setup
dataset_name = 'lcquad'

import os
from setup import IndexSearch, Mongo_Connector, load_embeddings

e_vectors = load_embeddings('fasttext_e_labels')
e_index = IndexSearch('dbpedia201604e')
mongo = Mongo_Connector('kbqa', dataset_name)

# match and save matched entity URIs to MongoDB
loaded = False

limit = None
string_cutoff = 50  # maximum number of candidate entities per mention
semantic_cutoff = 1000
max_degree = 50000
max_triples = 10000

# path to KG relations
from hdt import HDTDocument
hdt_path = "/home/zola/Projects/hdt-cpp-molecules/libhdt/data/"
hdt_file = 'dbpedia2016-04en.hdt'
namespace = "http://dbpedia.org/"

Exemple #2

Fichier : final_benchmark_results_cache.py Projet : fubincom/KBQA

Created on Feb 20, 2018

.. codeauthor: svitlana vakulenko
    <*****@*****.**>

Final evaluation script for comparison with the benchmark
'''

# setup
dataset_name = 'lcquad'
embeddings_choice = 'glove840B300d'

# connect to DB storing the dataset
from setup import Mongo_Connector, load_embeddings, IndexSearch

mongo = Mongo_Connector('kbqa', dataset_name)

# path to KG relations
from hdt import HDTDocument

hdt_path = "/home/zola/Projects/hdt-cpp-molecules/libhdt/data/"
hdt_file = 'dbpedia2016-04en.hdt'
namespace = "predef-dbpedia2016-04"

import time
from collections import defaultdict
import numpy as np
import scipy.sparse as sp

# entity and predicate catalogs
e_index = IndexSearch('dbpedia201604e')

Exemple #3

Fichier : final_benchmark_results_time_size2.py Projet : svakulenk0/KBQA

'''
Created on Feb 20, 2018

.. codeauthor: svitlana vakulenko
    <*****@*****.**>

Final evaluation script for comparison with the benchmark
'''

# setup
dataset_name = 'lcquad'
embeddings_choice = 'glove840B300d'

# connect to DB storing the dataset
from setup import Mongo_Connector, load_embeddings, IndexSearch
mongo = Mongo_Connector('kbqa', dataset_name)

# path to KG relations
from hdt import HDTDocument
# hdt_path = "/home/zola/Projects/hdt-cpp-molecules/libhdt/data/"
hdt_path = "/mnt/ssd/sv/"
hdt_file = 'dbpedia2016-04en.hdt'
# namespace = "http://dbpedia.org/"
namespace = "predef-dbpedia2016-04"

import time
from collections import defaultdict
import numpy as np
import scipy.sparse as sp

# entity and predicate catalogs