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