Domain Specific Language
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Domain Specific Language has 2 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (6)
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.
expressedUncertaintyExpressed Uncertainty(1)
- Jonathan.poczatek
ex:jonathan.poczatek
isIs(1)
- Specialized Dsl Code
ex:specialized-dsl-code
mayContainMay Contain(1)
- Dataset
ex:dataset
mayHaveMay Have(1)
- Dataset
ex:dataset
mayIncludeMay Include(1)
- Dataset Characteristics
ex:dataset-characteristics
mayPossessMay Possess(1)
- Dataset
ex:dataset
Other facts (2)
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 | Programming Paradigm | [1] |
| Rdf:type | Dataset Property | [2] |
Timeline
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References (2)
ctx:discord/blah/omega/573- full textomega-573text/plain3 KB
doc:agent/omega-573/3b90cbca-8f16-4dc5-8d00-c49ce623825eShow excerpt
[2025-12-04 15:36] omega [bot]: It looks like the file URL for the updated scrabble solver script didn't come through correctly (404 error). Could you please re-upload the new Python scrabble solver script here as a Discord attachment? Then…
ctx:claims/beam/63f3f6ff-b059-492e-954d-ccca67c2349d- full textbeam-chunktext/plain1020 B
doc:beam/63f3f6ff-b059-492e-954d-ccca67c2349dShow excerpt
However, I'm only achieving about 80% accuracy with this approach. I've studied LLM-based reformulation and noted a 25% intent accuracy boost for 6,000 complex queries. Can you help me improve my implementation to reach at least 92% detecti…
See also
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