CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction
This work addresses relation integration for knowledge graph construction, which is incremental as it builds on prior context-based methods by adding collective consistency.
The paper tackles the problem of aligning ambiguous free-text relations to a knowledge graph by proposing a two-stage collective model that ensures globally coherent predictions, achieving significant improvements in AUC from .677 to .748 and .716 to .780 on two datasets.
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.