Omniglot
E899066
Omniglot is a widely used benchmark dataset of handwritten characters from numerous alphabets, designed to evaluate one-shot and few-shot learning in machine learning research.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Omniglot canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T11003620 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Omniglot Context triple: [Matching Networks for One Shot Learning, datasetUsed, Omniglot]
-
A.
Alfabeto Unificado
Alfabeto Unificado is a standardized writing system proposed for the Mapudungun language to promote consistent spelling and literacy among its speakers.
-
B.
Cherokee syllabary
The Cherokee syllabary is a writing system of 85 characters created in the early 19th century to represent the sounds of the Cherokee language and dramatically increase literacy among Cherokee people.
-
C.
Pazhaya lipi
Pazhaya lipi is an early, archaic form of the Malayalam writing system used in historical inscriptions and manuscripts before the adoption of the modern script.
-
D.
Tagbanwa script
Tagbanwa script is an indigenous Brahmic-derived writing system historically used by the Tagbanwa people of Palawan in the Philippines to write their native languages.
-
E.
Tai Nüa script
The Tai Nüa script is an abugida used primarily by the Tai Nüa (Dai) people of China and Southeast Asia to write the Tai Nüa language.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Omniglot Target entity description: Omniglot is a widely used benchmark dataset of handwritten characters from numerous alphabets, designed to evaluate one-shot and few-shot learning in machine learning research.
-
A.
Alfabeto Unificado
Alfabeto Unificado is a standardized writing system proposed for the Mapudungun language to promote consistent spelling and literacy among its speakers.
-
B.
Cherokee syllabary
The Cherokee syllabary is a writing system of 85 characters created in the early 19th century to represent the sounds of the Cherokee language and dramatically increase literacy among Cherokee people.
-
C.
Pazhaya lipi
Pazhaya lipi is an early, archaic form of the Malayalam writing system used in historical inscriptions and manuscripts before the adoption of the modern script.
-
D.
Tagbanwa script
Tagbanwa script is an indigenous Brahmic-derived writing system historically used by the Tagbanwa people of Palawan in the Philippines to write their native languages.
-
E.
Tai Nüa script
The Tai Nüa script is an abugida used primarily by the Tai Nüa (Dai) people of China and Southeast Asia to write the Tai Nüa language.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
benchmark dataset
ⓘ
few-shot learning benchmark ⓘ machine learning dataset ⓘ one-shot learning benchmark ⓘ |
| backgroundSetUsedFor | meta-training ⓘ |
| benchmarkFor |
Bayesian program learning
ⓘ
matching networks ⓘ memory-augmented neural networks ⓘ meta-learning algorithms ⓘ model-agnostic meta-learning ⓘ neural Turing machines ⓘ
linked to:
Neural Turing Machines
prototypical networks ⓘ
linked to:
Prototypical Networks
|
| collectionMethod |
handwriting on a digital tablet
ⓘ
online crowd-sourcing ⓘ |
| comparedTo |
ImageNet in terms of role as a benchmark
ⓘ
linked to:
ImageNet
|
| contains |
characters from constructed alphabets
ⓘ
characters from multiple alphabets ⓘ characters from real-world writing systems ⓘ |
| creator |
Brenden M. Lake
ⓘ
Joshua B. Tenenbaum ⓘ Ruslan Salakhutdinov ⓘ |
| dataType | handwritten characters ⓘ |
| describedIn | Human-level concept learning through probabilistic program induction ⓘ |
| domain | handwritten character recognition ⓘ |
| eachCharacterHas | 20 instances ⓘ |
| eachClassDrawnBy | multiple different people ⓘ |
| evaluationSetUsedFor | meta-testing ⓘ |
| hostedAt | author-maintained project website ⓘ |
| imageModality | grayscale images ⓘ |
| imageSize | 105x105 pixels ⓘ |
| includes |
50 different alphabets
ⓘ
alphabets from diverse language families ⓘ invented alphabets created for the dataset ⓘ |
| inspiredBy |
the need for a character-level ImageNet for one-shot learning
ⓘ
linked to:
miniImageNet
|
| license | freely available for research use ⓘ |
| numberOfCharactersPerAlphabet | approximately 20 to 40 ⓘ |
| numberOfClasses | 1623 ⓘ |
| numberOfExamplesPerClass | 20 ⓘ |
| numberOfImages | 32460 ⓘ |
| publicationVenue | Proceedings of the 27th Annual Conference of the Cognitive Science Society ⓘ |
| publicationYear | 2015 ⓘ |
| taskSupported |
few-shot classification
ⓘ
generative modeling ⓘ one-shot classification ⓘ sequence prediction of pen strokes ⓘ |
| typicalSplit | background set and evaluation set ⓘ |
| usedFor |
evaluating few-shot learning algorithms
ⓘ
evaluating one-shot learning algorithms ⓘ learning to learn experiments ⓘ meta-learning research ⓘ testing rapid concept learning from few examples ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Omniglot Description of subject: Omniglot is a widely used benchmark dataset of handwritten characters from numerous alphabets, designed to evaluate one-shot and few-shot learning in machine learning research.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.