  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.
