def __init__(self, result_limit=5000, expansion=True, show_query=False):
self._expansion = expansion
self._show_query = show_query
self._linker = NameSPARQL()
self._extracter = Extraction()
self._yago = YagoTypeSimilarity()
self._query_graph = QueryGraph(result_limit)
def test_extraction():
from sematch.nlp import Extraction
from sematch.sparql import EntityFeatures
entity_f = EntityFeatures()
yin_and_yang = entity_f.features(
'http://dbpedia.org/resource/Yin_and_yang')
assert yin_and_yang is not None
extract = Extraction()
assert 'Chinese' in extract.extract_chunks_doc(yin_and_yang['abstract'])
assert 'philosophy' in extract.extract_words_doc(yin_and_yang['abstract'])
def test_extraction():
from sematch.nlp import Extraction
from sematch.semantic.sparql import EntityFeatures
upm = EntityFeatures().features('http://dbpedia.org/resource/Technical_University_of_Madrid')
extract = Extraction()
assert extract.extract_nouns(upm['abstract']) is not None
assert extract.extract_verbs(upm['abstract']) is not None
assert extract.extract_chunks_doc(upm['abstract']) is not None
cats = extract.category_features(upm['category'])
assert extract.category2words(cats) is not None
def __init__(self, result_limit=5000, expansion=False, show_query=False):
""" semantic search of entities and concepts
:param result_limit: maximumn number of retrieved entities
:param expansion: if conduct concept expansion
:param show_query: if SPARQL query is shown
"""
self._expansion = expansion
self._show_query = show_query
self._linker = NameSPARQL()
self._extracter = Extraction()
self._yago = YagoTypeSimilarity()
self._query_graph = QueryGraph(result_limit)
def test_sim_graph():
from sematch.semantic.graph import SimGraph
from sematch.semantic.similarity import WordNetSimilarity
from sematch.nlp import Extraction, lemmatization
from sematch.sparql import EntityFeatures
from collections import Counter
madrid = EntityFeatures().features(
'http://dbpedia.org/resource/Tom_Cruise')
words = Extraction().extract_words_sent(madrid['abstract'])
words = list(set(lemmatization(words)))
wns = WordNetSimilarity()
word_graph = SimGraph(words, wns.word_similarity)
word_scores = word_graph.page_rank()
words, scores = zip(*Counter(word_scores).most_common(10))
assert words is not None
def __init__(self):
self._linker = NameSPARQL()
self._extracter = Extraction()
self._yago = YagoTypeSimilarity()
self._query_graph = QueryGraph()
