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--- |
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language: |
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- en |
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license: |
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- other |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 1<n<1K |
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pretty_name: Relation Mapping |
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--- |
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# Dataset Card for "relbert/relation_mapping" |
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## Dataset Description |
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- **Repository:** [RelBERT](https://github.com/asahi417/relbert) |
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- **Paper:** [https://www.jair.org/index.php/jair/article/view/10583](https://www.jair.org/index.php/jair/article/view/10583) |
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- **Dataset:** Relation Mapping |
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### Dataset Summary |
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Relation Mapping 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)). |
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Relation mapping `M` is the set of bijective map in between two sets of terms (`A` and `B`): |
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``` |
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[set `A`]: ("solar system", "sun", "planet", "mass", "attracts", "revolves", "gravity") |
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[set `B`]: ("atom", "nucleus", "electron", "charge", "attracts", "revolves", "electromagnetism") |
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[Relation Mapping `M`] |
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* "solar system" -> "atom" |
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* "sun" -> "nucleus" |
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* "planet" -> "electron" |
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* "mass" -> "charge" |
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* "attracts" -> "attracts" |
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* "revolves" -> "revolves" |
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* "gravity" -> "electromagnetism" |
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``` |
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***[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`. |
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## Dataset Structure |
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### Data Instances |
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An example looks as follows. |
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``` |
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{ |
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"id": "m10", |
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"reference": ["seeing", "understanding"], |
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"source": ["seeing", "light", "illuminating", "darkness", "view", "hidden"], |
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"target": ["understanding", "knowledge", "explaining", "confusion", "interpretation", "secret"], |
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"agreement": [68.2, 77.3, 86.4, 86.4, 68.2, 86.4], |
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"pos": ["vbg", "nn", "vbg", "nn", "nn", "jj"], |
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"target_random": ["knowledge", "interpretation", "explaining", "confusion", "understanding", "secret"] |
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} |
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``` |
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- `source`: A list of terms, which is the source of the relation mapping from. |
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- `target_random`: A list of terms, where we want to find a mapping from `source` to. |
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- `target`: A correctly ordered `target_random` that aligns with the `source`. |
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Given `source` and `target_random`, the task is to predict the correct order of `target_random` so that it matches `target`. |
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In average 7 terms are in the set, so the total number of possible order is 5040. |
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### Data Splits |
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| name |test| |
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|---------|----:| |
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|relation_mapping| 20 | |
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### Citation Information |
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``` |
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@article{turney2008latent, |
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title={The latent relation mapping engine: Algorithm and experiments}, |
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author={Turney, Peter D}, |
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journal={Journal of Artificial Intelligence Research}, |
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volume={33}, |
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pages={615--655}, |
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year={2008} |
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} |
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``` |
