token synonym substitution
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
token synonym substitution has 8 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
Mostly:rdf:type(3), replaces(1), with(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (5)
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.
hasTrueBranchHas True Branch(1)
- Dictionary Membership Check
ex:dictionary-membership-check
isModifiedByIs Modified by(1)
- Query
ex:query
isUsedForIs Used for(1)
- Dictionary
ex:dictionary
resultsInResults in(1)
- Dictionary Lookup
ex:dictionary-lookup
usesUses(1)
- Query Expansion
ex:query-expansion
Other facts (7)
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 | Operation | [1] |
| Rdf:type | Replacement Operation | [1] |
| Rdf:type | Expansion Method | [2] |
| Replaces | Token | [1] |
| With | Dictionary Value | [1] |
| Results in | Modified Query | [1] |
| Uses | Dictionary | [1] |
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 (2)
ctx:claims/beam/12312cab-c28d-4376-a351-2e8169a3598f- full textbeam-chunktext/plain1 KB
doc:beam/12312cab-c28d-4376-a351-2e8169a3598fShow excerpt
By following these steps, you can effectively manage your remaining workload and ensure that the query rewriting code is completed within a reasonable timeframe. Let me know if you need further assistance or have any specific concerns! [Tu…
ctx:claims/beam/b27efc86-7008-4384-852a-049d06d255cb- full textbeam-chunktext/plain1 KB
doc:beam/b27efc86-7008-4384-852a-049d06d255cbShow excerpt
entities = [(ent.text, ent.label_) for ent in doc.ents] # Extract synonyms for each token synonyms = [] for token in tokens: pos = get_wordnet_pos(nltk.pos_tag([token])[0][1]) synsets = wordnet.synsets(t…
See also
Keep researching
Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.