# Dataset Card for "relbert/scientific_and_creative_analogy"
## Dataset Description
- **Repository:** [https://github.com/taczin/SCAN_analogies](https://github.com/taczin/SCAN_analogies)
- **Paper:** [https://arxiv.org/abs/2211.15268](https://arxiv.org/abs/2211.15268)
- **Dataset:** Relation Mapping

### Dataset Summary
A dataset for relation mapping task, which is a task to choose optimal combination of word pairs (see more detail in the [paper](https://www.jair.org/index.php/jair/article/view/10583)).


Relation mapping `M` is the set of bijective map in between two sets of terms (`A` and `B`):
```
[set `A`]: ("solar system", "sun", "planet", "mass", "attracts", "revolves", "gravity")
[set `B`]: ("atom", "nucleus", "electron", "charge", "attracts", "revolves", "electromagnetism")

[Relation Mapping `M`]
* "solar system"   -> "atom"
* "sun"            -> "nucleus"
* "planet"         -> "electron"
* "mass"           -> "charge"
* "attracts"       -> "attracts"
* "revolves"       -> "revolves"
* "gravity"        -> "electromagnetism"
```

***[Relation Mapping Problem](https://www.jair.org/index.php/jair/article/view/10583)*** is the task to identify the mapping `M` given the sets of terms `A` and `B`.


## Dataset Structure
### Data Instances
An example looks as follows.
```
{
 "id": "0",
 "reference": ["buying an item", "accepting a belief"],
 "source": ["buying an item", "buyer", "merchandise", "buying", "selling", "returning", "valuable", "worthless"],
 "target": ["accepting a belief", "believer", "belief", "accepting", "advocating", "rejecting", "true", "false"],
 "target_random": ["rejecting", "true", "false", "accepting a belief", "believer", "advocating", "belief", "accepting"],
 "type": "metaphor"
}
```

- `source`: A list of terms, which is the source of the relation mapping from.
- `target_random`: A list of terms, where we want to find a mapping from `source` to.
- `target`: A correctly ordered `target_random` that aligns with the `source`.

Given `source` and `target_random`, the task is to predict the correct order of `target_random` so that it matches `target`.
In average 7 terms are in the set, so the total number of possible order is 5040.


### Data Splits
|  name   |test|
|---------|----:|
|relation_mapping| 45 |


### Citation Information
```
@article{czinczoll2022scientific,
  title={Scientific and Creative Analogies in Pretrained Language Models},
  author={Czinczoll, Tamara and Yannakoudakis, Helen and Mishra, Pushkar and Shutova, Ekaterina},
  journal={arXiv preprint arXiv:2211.15268},
  year={2022}
}
```
multilinguality:monolingual size_categories:1<n<1K language:en license:other arxiv:2211.15268 region:us
{"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1<n<1K"], "pretty_name": "Relation Mapping"}
TAGS
#multilinguality-monolingual #size_categories-1<n<1K #language-English #license-other #arxiv-2211.15268 #region-us 

Dataset Card for "relbert/scientific\_and\_creative\_analogy"
=============================================================


Dataset Description
-------------------


* Repository: URL
* Paper: URL
* Dataset: Relation Mapping


### Dataset Summary


A dataset for relation mapping task, which is a task to choose optimal combination of word pairs (see more detail in the paper).


Relation mapping 'M' is the set of bijective map in between two sets of terms ('A' and 'B'):


*Relation Mapping Problem* is the task to identify the mapping 'M' given the sets of terms 'A' and 'B'.


Dataset Structure
-----------------


### Data Instances


An example looks as follows.


* 'source': A list of terms, which is the source of the relation mapping from.
* 'target\_random': A list of terms, where we want to find a mapping from 'source' to.
* 'target': A correctly ordered 'target\_random' that aligns with the 'source'.


Given 'source' and 'target\_random', the task is to predict the correct order of 'target\_random' so that it matches 'target'.
In average 7 terms are in the set, so the total number of possible order is 5040.


### Data Splits




### Dataset Summary


A dataset for relation mapping task, which is a task to choose optimal combination of word pairs (see more detail in the paper).


Relation mapping 'M' is the set of bijective map in between two sets of terms ('A' and 'B'):


*Relation Mapping Problem* is the task to identify the mapping 'M' given the sets of terms 'A' and 'B'.


Dataset Structure
----------------- ### Data Instances


An example looks as follows.


* 'source': A list of terms, which is the source of the relation mapping from.
* 'target\_random': A list of terms, where we want to find a mapping from 'source' to.
* 'target': A correctly ordered 'target\_random' that aligns with the 'source'.


Given 'source' and 'target\_random', the task is to predict the correct order of 'target\_random' so that it matches 'target'.
In average 7 terms are in the set, so the total number of possible order is 5040. ### Data Splits
TAGS
#multilinguality-monolingual #size_categories-1<n<1K #language-English #license-other #arxiv-2211.15268 #region-us 
 ### Dataset Summary


A dataset for relation mapping task, which is a task to choose optimal combination of word pairs (see more detail in the paper).


Relation mapping 'M' is the set of bijective map in between two sets of terms ('A' and 'B'):


*Relation Mapping Problem* is the task to identify the mapping 'M' given the sets of terms 'A' and 'B'.


Dataset Structure
----------------- ### Data Instances


An example looks as follows.


* 'source': A list of terms, which is the source of the relation mapping from.
* 'target\_random': A list of terms, where we want to find a mapping from 'source' to.
* 'target': A correctly ordered 'target\_random' that aligns with the 'source'.


Given 'source' and 'target\_random', the task is to predict the correct order of 'target\_random' so that it matches 'target'.
In average 7 terms are in the set, so the total number of possible order is 5040. ### Data Splits