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.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_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 test_entity_features():
from sematch.sparql import EntityFeatures
entity_f = EntityFeatures()
yin_and_yang = entity_f.features(
'http://dbpedia.org/resource/Yin_and_yang')
print yin_and_yang
def test_entity_features():
from sematch.sparql import EntityFeatures
entity_f = EntityFeatures()
yin_and_yang = entity_f.features('http://dbpedia.org/resource/Yin_and_yang')
print yin_and_yang