  We consider the problem of learning knowledge graph (KG) embeddings for
entity alignment (EA). Current methods use the embedding models mainly focusing
on triple-level learning, which lacks the ability of capturing long-term
dependencies existing in KGs. Consequently, the embedding-based EA methods
heavily rely on the amount of prior (known) alignment, due to the identity
information in the prior alignment cannot be efficiently propagated from one KG
to another. In this paper, we propose RSN4EA (recurrent skipping networks for
EA), which leverages biased random walk sampling for generating long paths
across KGs and models the paths with a novel recurrent skipping network (RSN).
RSN integrates the conventional recurrent neural network (RNN) with residual
learning and can largely improve the convergence speed and performance with
only a few more parameters. We evaluated RSN4EA on a series of datasets
constructed from real-world KGs. Our experimental results showed that it
outperformed a number of state-of-the-art embedding-based EA methods and also
achieved comparable performance for KG completion.
