  In the last few years, the interest in knowledge bases has grown
exponentially in both the research community and the industry due to their
essential role in AI applications. Entity alignment is an important task for
enriching knowledge bases. This paper provides a comprehensive tutorial-type
survey on representative entity alignment techniques that use the new approach
of representation learning. We present a framework for capturing the key
characteristics of these techniques, propose two datasets to address the
limitation of existing benchmark datasets, and conduct extensive experiments
using the proposed datasets. The framework gives a clear picture of how the
techniques work. The experiments yield important results about the empirical
performance of the techniques and how various factors affect the performance.
One important observation not stressed by previous work is that techniques
making good use of attribute triples and relation predicates as features stand
out as winners.
