thesaurus data
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
thesaurus data has 5 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (2)
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.
Other facts (4)
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 | Data Resource | [1] |
| Rdf:type | Data Structure | [2] |
| Input to | Thesaurus Integration | [1] |
| Structure | dictionary | [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 (2)
ctx:claims/beam/7555ca4b-6a28-4b87-bfc7-43ee084a5ca2- full textbeam-chunktext/plain1 KB
doc:beam/7555ca4b-6a28-4b87-bfc7-43ee084a5ca2Show excerpt
By following these steps, you can integrate a more advanced NLP model for synonym expansion, leading to more accurate and contextually relevant results. If you have any specific issues or need further customization, feel free to ask! [Turn…
ctx:claims/beam/5e1fccc0-109f-4d58-b6c4-6482a168aad7- full textbeam-chunktext/plain1 KB
doc:beam/5e1fccc0-109f-4d58-b6c4-6482a168aad7Show excerpt
for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_contextual_embeddings(syn)) for syn in synonyms] closest_synonyms.extend([synon…
See also
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