distance
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
distance has 12 facts recorded in Dontopedia across 6 references, with 2 live disagreements.
Mostly:rdf:type(5), called with(2), belongs(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (9)
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.
callsFunctionCalls Function(2)
- Find Closest Match
ex:find-closest-match - Spelling Correction
ex:spelling-correction
isArgumentToIs Argument to(2)
- Dictionary Parameter
ex:dictionary-parameter - Word Parameter
ex:word-parameter
providesProvides(2)
- Levenshtein Library
ex:levenshtein-library - Levenshtein Module
ex:Levenshtein-module
calledOnCalled on(1)
- Distance Call
ex:distance-call
usesUses(1)
- Find Closest Match Function
ex:find-closest-match-function
usesDistanceMetricUses Distance Metric(1)
- Find Closest Match
ex:find-closest-match
Other facts (10)
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 | Function | [1] |
| Rdf:type | Distance Function | [2] |
| Rdf:type | Function | [3] |
| Rdf:type | Library Function | [4] |
| Rdf:type | Undefined Function | [6] |
| Called With | word | [5] |
| Called With | dict_word | [5] |
| Belongs | Levenshtein Module | [1] |
| Used by | Find Closest Match | [2] |
| Is Undefined in | Script | [2] |
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 (6)
ctx:claims/beam/ec325d43-e9a5-4bd8-934d-599822520612ctx:claims/beam/a8d4e00d-0adb-49c2-a304-e8356b9d69a3- full textbeam-chunktext/plain1 KB
doc:beam/a8d4e00d-0adb-49c2-a304-e8356b9d69a3Show excerpt
model = BertForMaskedLM.from_pretrained('bert-base-uncased') def find_closest_match(word, dictionary, threshold=2): """ Find the closest match in the dictionary using the specified threshold. """ min_distance = float('inf')…
ctx:claims/beam/3b8e94e6-6ea2-40ce-b7fd-ddc4e92b2865- full textbeam-chunktext/plain1 KB
doc:beam/3b8e94e6-6ea2-40ce-b7fd-ddc4e92b2865Show excerpt
dist = distance(word, dict_word) if dist < min_distance and dist <= threshold: min_distance = dist closest_word = dict_word return closest_word ``` #### 3. Optimize Spell Correction Logic ```pyt…
ctx:claims/beam/dbb91cd4-736d-4452-9b19-46651567b10b- full textbeam-chunktext/plain1 KB
doc:beam/dbb91cd4-736d-4452-9b19-46651567b10bShow excerpt
Here's an example of how you can implement these best practices in Python: #### 1. Use Efficient Data Structures ```python class TrieNode: def __init__(self): self.children = {} self.is_end_of_word = False class Trie:…
ctx:claims/beam/3cb97947-2304-4ba1-a2c5-598750f9b2f9- full textbeam-chunktext/plain1 KB
doc:beam/3cb97947-2304-4ba1-a2c5-598750f9b2f9Show excerpt
dist = distance(word, dict_word) if dist < min_distance and dist <= threshold: min_distance = dist closest_word = dict_word return closest_word tokenizer = BertTokenizer.from_pretrained('bert-bas…
ctx:claims/beam/679660b6-e3c2-4219-8f8c-2598b5c9e898
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