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query_gen.py
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query_gen.py
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from parser.lc_quad_linked import LC_Qaud_Linked
from parser.webqsp import WebQSP
from parser.qald import Qald
from common.container.sparql import SPARQL
from common.container.answerset import AnswerSet
from common.graph.graph import Graph
from common.utility.stats import Stats
from common.query.querybuilder import QueryBuilder
import common.utility.utility as utility
from linker.goldLinker import GoldLinker
from linker.earl import Earl
from learning.classifier.svmclassifier import SVMClassifier
from learning.classifier.naivebayesclassifier import NaiveBayesClassifier
import json
import argparse
import logging
import sys
import os
def qg(linker, kb, parser, qapair, question_type_classifier, double_relation_classifier, force_gold=True):
logger.info(qapair.sparql)
logger.info(qapair.question.text)
# Get Answer from KB online
status, raw_answer_true = kb.query(qapair.sparql.query.replace("https", "http"))
answerset_true = AnswerSet(raw_answer_true, parser.parse_queryresult)
qapair.answerset = answerset_true
ask_query = "ASK " in qapair.sparql.query
count_query = "COUNT(" in qapair.sparql.query
sort_query = "order by" in qapair.sparql.raw_query.lower()
entities, ontologies = linker.do(qapair, force_gold=force_gold)
if entities is None or ontologies is None:
return "-Linker_failed", []
graph = Graph(kb)
queryBuilder = QueryBuilder()
logger.info("start finding the minimal subgraph")
graph.find_minimal_subgraph(entities, ontologies, ask_query=ask_query, sort_query=sort_query)
logger.info(graph)
wheres = queryBuilder.to_where_statement(graph, parser.parse_queryresult, ask_query=ask_query,
count_query=count_query, sort_query=sort_query)
output_where = [{"query": " .".join(item["where"]), "correct": False, "target_var": "?u_0"} for item in wheres]
for item in list(output_where):
logger.info(item["query"])
if len(wheres) == 0:
return "-without_path", output_where
correct = False
for idx in range(len(wheres)):
where = wheres[idx]
if "answer" in where:
answerset = where["answer"]
target_var = where["target_var"]
else:
target_var = "?u_" + str(where["suggested_id"])
raw_answer = kb.query_where(where["where"], target_var, count_query, ask_query)
answerset = AnswerSet(raw_answer, parser.parse_queryresult)
output_where[idx]["target_var"] = target_var
sparql = SPARQL(kb.sparql_query(where["where"], target_var, count_query, ask_query), ds.parser.parse_sparql)
if (answerset == qapair.answerset) != (sparql == qapair.sparql):
print("error")
if answerset == qapair.answerset:
correct = True
output_where[idx]["correct"] = True
output_where[idx]["target_var"] = target_var
else:
if target_var == "?u_0":
target_var = "?u_1"
else:
target_var = "?u_0"
raw_answer = kb.query_where(where["where"], target_var, count_query, ask_query)
print("Q_H ",)
print(raw_answer)
print("Q_")
answerset = AnswerSet(raw_answer, parser.parse_queryresult)
sparql = SPARQL(kb.sparql_query(where["where"], target_var, count_query, ask_query), ds.parser.parse_sparql)
if (answerset == qapair.answerset) != (sparql == qapair.sparql):
print("error")
if answerset == qapair.answerset:
correct = True
output_where[idx]["correct"] = True
output_where[idx]["target_var"] = target_var
return "correct" if correct else "-incorrect", output_where
if __name__ == "__main__":
logger = logging.getLogger(__name__)
utility.setup_logging()
parser = argparse.ArgumentParser(description='Generate SPARQL query')
parser.add_argument("--ds", help="0: LC-Quad, 1: WebQuestions", type=int, default=0, dest="dataset")
parser.add_argument("--path", help="dataset path", default="./data/LC-QUAD/linked_answer6.json",
dest="dataset_path")
parser.add_argument("--file", help="file name to save the results", default="tmp", dest="file_name")
parser.add_argument("--in", help="only works on this list", type=int, nargs='+', default=[], dest="list_id")
parser.add_argument("--max", help="max threshold", type=int, default=-1, dest="max")
parser.add_argument("--linker", help="0: gold linker, 1: EARL+force gold, 2: EARL, 3: (RelN, TagMe)", type=int,
default=0, dest="linker")
parser.add_argument("--classifier", help="'svm' (default) or 'naivebayes'", default="svm", dest="classifier")
args = parser.parse_args()
base_dir = "./output"
question_type_classifier_path = os.path.join(base_dir, "question_type_classifier")
double_relation_classifier_path = os.path.join(base_dir, "double_relation_classifier")
if args.classifier == "svm":
question_type_classifier = SVMClassifier(os.path.join(question_type_classifier_path, "svm.model"))
double_relation_classifier = SVMClassifier(os.path.join(double_relation_classifier_path, "svm.model"))
elif args.classifier == "naivebayes":
question_type_classifier = NaiveBayesClassifier(os.path.join(question_type_classifier_path, "naivebayes.model"))
double_relation_classifier = NaiveBayesClassifier(os.path.join(double_relation_classifier_path, "svm.model"))
stats = Stats()
t = args.dataset
output_file = args.file_name
if args.linker == 0:
linker = GoldLinker()
elif args.linker == 3:
linker = 3
else:
linker = Earl()
if t == 0:
ds = LC_Qaud_Linked(path=args.dataset_path)
ds.load()
ds.parse()
elif t == 1:
ds = WebQSP()
ds.load()
ds.parse()
ds.extend("./data/WebQuestionsSP/WebQSP.test.json")
elif t == 5:
ds = Qald(Qald.qald_5)
ds.load()
ds.parse()
elif t == 6:
ds = Qald(Qald.qald_6)
ds.load()
ds.parse()
elif t == 7:
ds = Qald(Qald.qald_7_largescale)
ds.load()
ds.parse()
ds.extend(Qald.qald_7_largescale_test)
elif t == 8:
ds = Qald(Qald.qald_7_multilingual)
ds.load()
ds.parse()
if not ds.parser.kb.server_available:
logger.error("Server is not available. Please check the endpoint at: {}".format(ds.parser.kb.endpoint))
sys.exit(0)
tmp = []
output = []
for qapair in ds.qapairs:
stats.inc("total")
output_row = {"question": qapair.question.text,
"id": qapair.id,
"query": qapair.sparql.query,
"answer": "",
"features": list(qapair.sparql.query_features()),
"generated_queries": []}
if qapair.answerset is None or len(qapair.answerset) == 0:
stats.inc("query_no_answer")
output_row["answer"] = "-no_answer"
else:
result, where = qg(linker, ds.parser.kb, ds.parser, qapair, question_type_classifier,
double_relation_classifier, args.linker == 1)
stats.inc(result)
output_row["answer"] = result
output_row["generated_queries"] = where
logger.info(result)
if args.max != -1 and stats["total"] > args.max:
break
# print "-" * 10
logger.info(stats)
# print "-" * 10
output.append(output_row)
if stats["total"] % 100 == 0:
with open("output/{}.json".format(output_file), "w") as data_file:
json.dump(output, data_file, sort_keys=True, indent=4, separators=(',', ': '))
with open("output/{}.json".format(output_file), "w") as data_file:
json.dump(output, data_file, sort_keys=True, indent=4, separators=(',', ': '))