# CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction Source: [CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction]() Collection index: [llms.txt]() Data updated: 2026-01-04T05:02:19.736Z Authors: Zhengbao Jiang, Jialong Han, Bunyamin Sisman, Xin Luna Dong Published: 2021-06-01T21:01:43.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Relation Integration on ReVerb + Freebase](): Relation Integration; ReVerb + Freebase; AUC: 0.748. - [Relation Integration on ReVerb + Wikidata](): Relation Integration; ReVerb + Wikidata; AUC: 0.78. ## Original sources and implementation [Original paper]() [PDF]()