Duplicate from KGraph/FB15k-237

Co-authored-by: YHLong <KGraph@users.noreply.huggingface.co>

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README.md

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+---
+annotations_creators:
+- found
+- crowdsourced
+language:
+- en
+language_creators: []
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: FB15k-237
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+tags:
+- knowledge graph
+- knowledge
+- link prediction
+- link
+task_categories:
+- other
+task_ids: []
+---
+
+# Dataset Card for FB15k-237
+
+## Table of Contents
+- [Dataset Card for FB15k-237](#dataset-card-for-fb15k-237)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [Personal and Sensitive Information](#personal-and-sensitive-information)
+  - [Considerations for Using the Data](#considerations-for-using-the-data)
+    - [Social Impact of Dataset](#social-impact-of-dataset)
+    - [Discussion of Biases](#discussion-of-biases)
+    - [Other Known Limitations](#other-known-limitations)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+    - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [https://deepai.org/dataset/fb15k-237](https://deepai.org/dataset/fb15k-237)
+- **Repository:** 
+- **Paper:** [More Information Needed](https://paperswithcode.com/dataset/fb15k-237)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+FB15k-237 is a link prediction dataset created from FB15k. While FB15k consists of 1,345 relations, 14,951 entities, and 592,213 triples, many triples are inverses that cause leakage from the training to testing and validation splits. FB15k-237 was created by Toutanova and Chen (2015) to ensure that the testing and evaluation datasets do not have inverse relation test leakage. In summary, FB15k-237 dataset contains 310,079 triples with 14,505 entities and 237 relation types.
+
+### Supported Tasks and Leaderboards
+
+Supported Tasks: link prediction task on knowledge graphs.
+
+Leaderboads:
+[More Information Needed](https://paperswithcode.com/sota/link-prediction-on-fb15k-237)
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+```
+@inproceedings{schlichtkrull2018modeling,
+  title={Modeling relational data with graph convolutional networks},
+  author={Schlichtkrull, Michael and Kipf, Thomas N and Bloem, Peter and Berg, Rianne van den and Titov, Ivan and Welling, Max},
+  booktitle={European semantic web conference},
+  pages={593--607},
+  year={2018},
+  organization={Springer}
+}
+```
+
+### Contributions
+
+Thanks to [@pp413](https://github.com/pp413) for adding this dataset.

data/FB15k_mid2description.txt

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data/README.txt

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+FB15K-237 Knowledge Base Completion Dataset
+
+This dataset contains knowledge base relation triples and textual mentions of Freebase entity pairs, as used in the work published in [1] and [2]. 
+The knowledge base triples are a subset of the FB15K set [3], originally derived from Freebase. The textual mentions are derived from 200 million sentences from the ClueWeb12 [5] corpus coupled with Freebase entity mention annotations [4].
+
+
+FILE FORMAT DETAILS
+
+The files train.txt, valid.txt, and test.text contain the training, development, and test set knowledge base triples used in both [1] and [2].
+The file text_cvsc.txt contains the textual triples used in [2] and the file text_emnlp.txt contains the textual triples used in [1].
+
+The knowledge base triples contain lines like this:
+
+/m/0grwj	/people/person/profession	/m/05sxg2
+
+The format is:
+
+mid1	relation	mid2
+
+The separator is a tab character; the mids are Freebase ids of entities, and the relation is a single or a two-hop relation from Freebase, where an intermediate complex value type entity has been collapsed out.
+
+The textual mentions files have lines like this:
+
+/m/02qkt        [XXX]:<-nn>:fact:<-pobj>:in:<-prep>:game:<-nsubj>:'s:<ccomp>:pivot:<nsubj>:[YYY]    /m/05sb1    3
+
+This indicates the mids of two Freebase entities, together with a fully lexicalized dependency path between the entities. The last element in the tuple is the number of occurrences of the specified entity pair with the given dependency path in sentences from ClueWeb12.
+The dependency paths are specified as sequences of words (like the word "fact" above) and labeled dependency links (like <nsubj> above). The direction of traversal of a dependency arc is indicated by whether there is a - sign in front of the arc label "e.g." <-nsubj> vs <nsubj>.
+
+
+REFERENCES
+
+[1] Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon. Representing text for joint embedding of text and knowledge bases.  In Proceedings of EMNLP 2015.
+[2] Kristina Toutanova and Danqi Chen. Observed versus latent features for knowledge base and text inference. In Proceedings of the 3rd Workshop on Continuous Vector Space Models and Their Compositionality 2015.
+[3] Antoine Bordes, Nicolas Usunier, Alberto Garcia Duran, Jason Weston, and Oksana Yakhnenko.  Translating embeddings for modeling multirelational data. In Advances in Neural Information Processing Systems (NIPS) 2013.
+[4] Evgeniy Gabrilovich, Michael Ringgaard, and Amarnag Subramanya. FACC1: Freebase annotation of ClueWeb corpora, Version 1 (release date 2013-06-26, format version 1, correction level 0). http://lemurproject.org/clueweb12/FACC1/
+[5] http://lemurproject.org/clueweb12/
+
+
+CONTACT
+
+Please contact Kristina Toutanova kristout@microsoft.com if you have questions about the dataset.

data/train.txt

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