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70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
# coding: utf-8
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"""Demonstrate how to build a simple knowledge base and run an Entity Linking algorithm.
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Currently still a bit of a dummy algorithm: taking simply the entity with highest probability for a given alias
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"""
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import spacy
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from spacy.kb import KnowledgeBase
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def create_kb(vocab):
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kb = KnowledgeBase(vocab=vocab)
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# adding entities
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entity_0 = "Q1004791"
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print("adding entity", entity_0)
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kb.add_entity(entity_id=entity_0, entity_name="Douglas", prob=0.5)
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entity_1 = "Q42"
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print("adding entity", entity_1)
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kb.add_entity(entity_id=entity_1, entity_name="Douglas Adams", prob=0.5)
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entity_2 = "Q5301561"
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print("adding entity", entity_2)
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kb.add_entity(entity_id=entity_2, entity_name="Douglas Haig", prob=0.5)
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# adding aliases
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print()
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alias_0 = "Douglas"
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print("adding alias", alias_0)
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kb.add_alias(alias=alias_0, entities=["Q1004791", "Q42", "Q5301561"], probabilities=[0.1, 0.6, 0.2])
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alias_1 = "Douglas Adams"
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print("adding alias", alias_1)
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kb.add_alias(alias=alias_1, entities=["Q42"], probabilities=[0.9])
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print()
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print("kb size:", len(kb), kb.get_size_entities(), kb.get_size_aliases())
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return kb
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def add_el(kb, nlp):
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el_pipe = nlp.create_pipe(name='el', config={"kb": kb})
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nlp.add_pipe(el_pipe, last=True)
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for alias in ["Douglas Adams", "Douglas"]:
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candidates = nlp.linker.kb.get_candidates(alias)
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print()
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print(len(candidates), "candidate(s) for", alias, ":")
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for c in candidates:
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print(" ", c.entity_id_, c.entity_name_, c.prior_prob)
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text = "In The Hitchhiker's Guide to the Galaxy, written by Douglas Adams, " \
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"Douglas reminds us to always bring our towel. " \
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"The main character in Doug's novel is called Arthur Dent."
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doc = nlp(text)
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print()
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for token in doc:
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print("token", token.text, token.ent_type_, token.ent_kb_id_)
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print()
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for ent in doc.ents:
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print("ent", ent.text, ent.label_, ent.kb_id_)
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if __name__ == "__main__":
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nlp = spacy.load('en_core_web_sm')
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my_kb = create_kb(nlp.vocab)
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add_el(my_kb, nlp)
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