# CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction

arXiv: https://arxiv.org/abs/2106.00793 · HTML: https://mlsanity.com/paper/2106.00793/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Self-training Improves Pre-training for Natural Language Understanding](https://mlsanity.com/paper/2010.02194/) (2020)
- [Incorporating External Knowledge through Pre-training for Natural Language to Code Generation](https://mlsanity.com/paper/2004.09015/) (2020)
- [CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information](https://mlsanity.com/paper/1902.00172/) (2019)
- [Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction](https://mlsanity.com/paper/1307.7973/) (2013)
- [Multilingual Relation Extraction using Compositional Universal Schema](https://mlsanity.com/paper/1511.06396/) (2015)
- [OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference](https://mlsanity.com/paper/1904.12606/) (2019)
- [Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema](https://mlsanity.com/paper/1606.05804/) (2016)
- [Unsupervised Methods for Determining Object and Relation Synonyms on the Web](https://mlsanity.com/paper/1401.5696/) (2014)

## Built on by (most cited first, 3 of 3)

- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [Linking Surface Facts to Large-Scale Knowledge Graphs](https://mlsanity.com/paper/2310.14909/) (2023)
- [Leveraging Data Augmentation for Process Information Extraction](https://mlsanity.com/paper/2404.07501/) (2024)

## Abstract (arXiv)

Integrating extracted knowledge from the Web to knowledge graphs (KGs) can facilitate tasks like question answering. We study relation integration that aims to align free-text relations in subject-relation-object extractions to relations in a target KG. To address the challenge that free-text relations are ambiguous, previous methods exploit neighbor entities and relations for additional context. However, the predictions are made independently, which can be mutually inconsistent. We propose a two-stage Collective Relation Integration (CoRI) model, where the first stage independently makes candidate predictions, and the second stage employs a collective model that accesses all candidate predictions to make globally coherent predictions. We further improve the collective model with augmented data from the portion of the target KG that is otherwise unused. Experiment results on two datasets show that CoRI can significantly outperform the baselines, improving AUC from .677 to .748 and from .716 to .780, respectively.