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Doc Variable has 8 facts recorded in Dontopedia across 2 references, with 2 live disagreements.
Mostly:processed by(3), rdf:type(2), result of(1)
assignedByrdfs:labelOther subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. “X motherOf this subject” — and answer questions the forward facts can't. Grouped by predicate.
createsVariableCreates Variable(1)ex:preprocess_textprovidesLemmatizationProvides Lemmatization(1)ex:en_core_web_smprovidesStopwordRemovalProvides Stopword Removal(1)ex:en_core_web_smprovidesTokenizationProvides Tokenization(1)ex:en_core_web_smTimeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
doc:beam/a35915ab-2696-4c7c-a4bb-e7554c72a063Here's an example of how you can use spaCy to preprocess a large volume of text: ```python import spacy import time # Load spaCy model nlp = spacy.load('en_core_web_sm') def preprocess_text(text): doc = nlp(text) tokens = [token.…
doc:beam/a290ecad-1619-4076-b8d8-0d36efc291f3# Process the query with spaCy doc = nlp(query) # Correct each word corrected_words = [] for token in doc: if not token.is_oov: corrected_words.append(token.text) else: correc…
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