corrected_words
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
corrected_words has 15 facts recorded in Dontopedia across 5 references, with 2 live disagreements.
Mostly:rdf:type(5), has member(3), populated by(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (7)
Other 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.
appliedToApplied to(1)
- Join Operation
join-operation
initializesInitializes(1)
- Corrected Words Initialization
ex:corrected-words-initialization
inputInput(1)
- Join Operation
ex:join-operation
joinsJoins(1)
- Correct Query Function
ex:correct-query-function
producesProduces(1)
- Spelling Correction
ex:spelling-correction
targetTarget(1)
- Append Operation
ex:append-operation
usesVariableUses Variable(1)
- Correct Query
ex:correct-query
Other facts (13)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Collection | [1] |
| Rdf:type | List | [2] |
| Rdf:type | List | [3] |
| Rdf:type | Variable | [4] |
| Rdf:type | List | [5] |
| Has Member | Looking | [1] |
| Has Member | Improve | [1] |
| Has Member | Spelling | [1] |
| Populated by | Closest Word Function | [2] |
| Element Type | String | [3] |
| Created by | Append Operation | [3] |
| Is Initialized As | Empty List | [4] |
| Data Structure | List | [5] |
Timeline
Timeline 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.
References (5)
ctx:claims/beam/e24dc3e9-d3c9-4c87-9eb2-f49f89b411ff- full textbeam-chunktext/plain1 KB
doc:beam/e24dc3e9-d3c9-4c87-9eb2-f49f89b411ffShow excerpt
correction_module.load_dictionary(dictionary_data) query = "I'm loking for a way to improove my spelng" corrected_query = correction_module.correct_spelling(query) print(corrected_query) # Output: "I'm looking for a way to improve my spel…
ctx:claims/beam/385414b9-deb5-4c17-9378-db347dcf89b3- full textbeam-chunktext/plain1 KB
doc:beam/385414b9-deb5-4c17-9378-db347dcf89b3Show excerpt
closest_word = find_closest_match(word, dictionary) if closest_word: corrected_words.append(closest_word) else: corrected_words.append(word) # Fallback to original word …
ctx:claims/beam/2e9fecea-ca91-4203-b029-db5f820e044actx:claims/beam/ba8f0f6e-4076-45ec-b8ac-81b951e5391d- full textbeam-chunktext/plain1 KB
doc:beam/ba8f0f6e-4076-45ec-b8ac-81b951e5391dShow excerpt
nltk.download('words') word_list = set(words.words()) # Define a function to correct a query using NLTK def correct_query_nltk(query): # Split the query into words words = query.split() # Correct each word corrected_wo…
ctx:claims/beam/9ab8fe53-eb32-42d9-8eac-c30e73177819
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