  Entity alignment (EA) identifies entities that refer to the same real-world
object but locate in different knowledge graphs (KGs), and has been harnessed
for KG construction and integration. When generating EA results, current
solutions treat entities independently and fail to take into account the
interdependence between entities. To fill this gap, we propose a collective EA
framework. We first employ three representative features, i.e., structural,
semantic and string signals, which are adapted to capture different aspects of
the similarity between entities in heterogeneous KGs. In order to make
collective EA decisions, we formulate EA as the classical stable matching
problem, which is further effectively solved by deferred acceptance algorithm.
Our proposal is evaluated on both cross-lingual and mono-lingual EA benchmarks
against state-of-the-art solutions, and the empirical results verify its
effectiveness and superiority.
