def init_from_config():
config_params = globals.config
sparql_backend = globals.get_sparql_backend(config_params)
query_extender = QueryCandidateExtender.init_from_config()
entity_linker = EntityLinker.init_from_config()
parser = CoreNLPParser.init_from_config()
scorer_obj = ranker.SimpleScoreRanker('DefaultScorer')
return QueryTranslator(sparql_backend, query_extender, entity_linker,
parser, scorer_obj)
def init_from_config():
config_params = globals.config
sparql_backend = globals.get_sparql_backend(config_params)
query_extender = QueryCandidateExtender.init_from_config()
entity_linker = EntityLinker.init_from_config()
parser = CoreNLPParser.init_from_config()
scorer_obj = ranker.SimpleScoreRanker('DefaultScorer')
return QueryTranslator(sparql_backend, query_extender,
entity_linker, parser, scorer_obj)
def get_from_config(cls, config_params):
sparql_backend = globals.get_sparql_backend(config_params)
query_extender = QueryCandidateExtender.init_from_config()
entity_linker = globals.get_entity_linker()
parser = globals.get_parser()
scorer_obj = ranker.SimpleScoreRanker('DefaultScorer')
ngram_notable_types_npmi_path = config_params.get('QueryCandidateExtender', 'ngram-notable-types-npmi', '')
notable_types_npmi_threshold = float(config_params.get('QueryCandidateExtender', 'notable-types-npmi-threshold'))
ngram_notable_types_npmi = None
if ngram_notable_types_npmi_path and os.path.exists(ngram_notable_types_npmi_path):
import cPickle as pickle
try:
with open(ngram_notable_types_npmi_path, 'rb') as inp:
logger.info("Loading types model from disk...")
ngram_notable_types_npmi = pickle.load(inp)
except IOError as exc:
logger.error("Error reading types model: %s" % str(exc))
ngram_notable_types_npmi = None
return SparqlQueryTranslator(sparql_backend, query_extender,
entity_linker, parser, scorer_obj,
ngram_notable_types_npmi,
notable_types_npmi_threshold)
