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Dataset Card for [Dataset Name]
Dataset Summary
The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome (Poon et al. 2014). This dataset was used in the paper “Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text” (Toutanova, Lin, Yih, Poon, and Quirk, 2016). More details can be found in the included README.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
English
Dataset Structure
NCI-PID-PubMed Genomics Knowledge Base Completion Dataset
This dataset includes a database of regulation relationships among genes and corresponding textual mentions of pairs of genes in PubMed article abstracts. The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome. This dataset was used in the paper "Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text".
FILE FORMAT DETAILS
The files train.txt, valid.txt, and test.text contain the training, development, and test set knowledge base (database of regulation relationships) triples used in. The file text.txt contains the textual triples derived from PubMed via entity linking and processing with Literome. The textual mentions were used for knowledge base completion in.
The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set).
The format is:
| GENE1 | relation | GENE2 |
Example: ABL1 Positive_regulation CDK2
The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set).
Data Instances
[More Information Needed]
Data Fields
The format is:
| GENE1 | relation | GENE2 |
Data Splits
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Dataset Creation
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Curation Rationale
[More Information Needed]
Source Data
[More Information Needed]
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
[More Information Needed]
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
[More Information Needed]
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
[More Information Needed]
Dataset Curators
The dataset was initially created by Kristina Toutanova, Victoria Lin, Wen-tau Yih, Hoifung Poon and Chris Quirk, during work done at Microsoft Research.
Licensing Information
[More Information Needed]
Citation Information
@inproceedings{toutanova-etal-2016-compositional,
title = "Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text",
author = "Toutanova, Kristina and
Lin, Victoria and
Yih, Wen-tau and
Poon, Hoifung and
Quirk, Chris",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P16-1136",
doi = "10.18653/v1/P16-1136",
pages = "1434--1444",
}
Contributions
Thanks to @manandey for adding this dataset.
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