Пример #1
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def test_query_graph():
    from sematch.sparql import QueryGraph
    qg = QueryGraph()
    concepts = ['http://dbpedia.org/class/yago/Film106262567',
                'http://dbpedia.org/class/yago/Film103339296',
                'http://dbpedia.org/class/yago/Film103338648',
                'http://dbpedia.org/class/yago/Movie106613686',
                'http://dbpedia.org/class/yago/Film103338821']
    entity = 'http://dbpedia.org/resource/Hal_Roach'
    assert 'http://dbpedia.org/resource/Luke_Pipes_the_Pippins' in qg.type_entity_query(concepts,entity)
Пример #2
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def test_query_graph():
    from sematch.sparql import QueryGraph
    qg = QueryGraph()
    concepts = [
        'http://dbpedia.org/class/yago/Film106262567',
        'http://dbpedia.org/class/yago/Film103339296',
        'http://dbpedia.org/class/yago/Film103338648',
        'http://dbpedia.org/class/yago/Movie106613686',
        'http://dbpedia.org/class/yago/Film103338821'
    ]
    entity = 'http://dbpedia.org/resource/Hal_Roach'
    assert 'http://dbpedia.org/resource/Luke_Pipes_the_Pippins' in qg.type_entity_query(
        concepts, entity)
Пример #3
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class EntitySearch:

    """This class is used for concept based entity search in DBpedia"""

    def __init__(self):
        self._linker = NameSPARQL()
        self._extracter = Extraction()
        self._yago = YagoTypeSimilarity()
        self._query_graph = QueryGraph()

    def query_process(self, query):
        """
        Process query into concept (common noun) and entity (proper noun). Link them
        to Knowledge Graph uri links respectively.
        :param query: short text query
        :return: tuple of concepts and entities in uris.
        """
        concepts = self._extracter.extract_words_sent(query)
        entities = self._extracter.extract_chunks_sent(query)
        concept_uris = list(itertools.chain.from_iterable(map(self._yago.word2yago, concepts)))
        entity_uris = list(itertools.chain.from_iterable(map(self._linker.name2entities, entities)))
        return list(set(concept_uris)), list(set(entity_uris))

    def search(self, query):
        results = []
        concepts, entities = self.query_process(query)
        for e in entities:
            for i in xrange(0, len(concepts), 5):
                results.extend(self._query_graph.type_entity_query(concepts[i:i + 5], e))
        return list(set(results))
Пример #4
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 def __init__(self):
     self._linker = NameSPARQL()
     self._extracter = Extraction()
     self._yago = YagoTypeSimilarity()
     self._query_graph = QueryGraph()