  Entity alignment is to find identical entities in different knowledge graphs.
Although embedding-based entity alignment has recently achieved remarkable
progress, training data insufficiency remains a critical challenge.
Conventional semi-supervised methods also suffer from the incorrect entity
alignment in newly proposed training data. To resolve these issues, we design
an iterative cycle-teaching framework for semi-supervised entity alignment. The
key idea is to train multiple entity alignment models (called aligners)
simultaneously and let each aligner iteratively teach its successor the
proposed new entity alignment. We propose a diversity-aware alignment selection
method to choose reliable entity alignment for each aligner. We also design a
conflict resolution mechanism to resolve the alignment conflict when combining
the new alignment of an aligner and that from its teacher. Besides, considering
the influence of cycle-teaching order, we elaborately design a strategy to
arrange the optimal order that can maximize the overall performance of multiple
aligners. The cycle-teaching process can break the limitations of each model's
learning capability and reduce the noise in new training data, leading to
improved performance. Extensive experiments on benchmark datasets demonstrate
the effectiveness of the proposed cycle-teaching framework, which significantly
outperforms the state-of-the-art models when the training data is insufficient
and the new entity alignment has much noise.

Artificial Intelligence;Computation and Language