  Entity alignment (EA) is the task of identifying the entities that refer to
the same real-world object but are located in different knowledge graphs (KGs).
For entities to be aligned, existing EA solutions treat them separately and
generate alignment results as ranked lists of entities on the other side.
Nevertheless, this decision-making paradigm fails to take into account the
interdependence among entities. Although some recent efforts mitigate this
issue by imposing the 1-to-1 constraint on the alignment process, they still
cannot adequately model the underlying interdependence and the results tend to
be sub-optimal. To fill in this gap, in this work, we delve into the dynamics
of the decision-making process, and offer a reinforcement learning (RL) based
model to align entities collectively. Under the RL framework, we devise the
coherence and exclusiveness constraints to characterize the interdependence and
restrict collective alignment. Additionally, to generate more precise inputs to
the RL framework, we employ representative features to capture different
aspects of the similarity between entities in heterogeneous KGs, which are
integrated by an adaptive feature fusion strategy. Our proposal is evaluated on
both cross-lingual and mono-lingual EA benchmarks and compared against
state-of-the-art solutions. The empirical results verify its effectiveness and
superiority.
