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README.md

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+---
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+pretty_name: link_prediction_nell_one
+---
+
+# Dataset Card for "relbert/link_prediction_nell_one"
+## Dataset Description
+- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
+- **Paper:** [https://aclanthology.org/D18-1223/](https://aclanthology.org/D18-1223/)
+- **Dataset:** Few-shots link prediction
+
+### Dataset Summary
+This is NELL-ONE dataset for the few-shots link prediction.
+
+## Dataset Structure
+### Data Instances
+An example of `test` looks as follows.
+```
+{
+    "relation": "concept:sportsgamesport",
+    "head": "concept:sportsgame:n1937_world_series",
+    "tail": "concept:sport:baseball"
+}
+```
+
+### Citation Information
+```
+@inproceedings{xiong-etal-2018-one,
+    title = "One-Shot Relational Learning for Knowledge Graphs",
+    author = "Xiong, Wenhan  and
+      Yu, Mo  and
+      Chang, Shiyu  and
+      Guo, Xiaoxiao  and
+      Wang, William Yang",
+    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
+    month = oct # "-" # nov,
+    year = "2018",
+    address = "Brussels, Belgium",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D18-1223",
+    doi = "10.18653/v1/D18-1223",
+    pages = "1980--1990",
+    abstract = "Knowledge graphs (KG) are the key components of various natural language processing applications. To further expand KGs{'} coverage, previous studies on knowledge graph completion usually require a large number of positive examples for each relation. However, we observe long-tail relations are actually more common in KGs and those newly added relations often do not have many known triples for training. In this work, we aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Empirically, our model yields considerable performance improvements over existing embedding models, and also eliminates the need of re-training the embedding models when dealing with newly added relations.",
+}
+```
+
+### LICENSE
+TBA