  Benefiting from the excellent ability of neural networks on learning semantic
representations, existing studies for entity linking (EL) have resorted to
neural networks to exploit both the local mention-to-entity compatibility and
the global interdependence between different EL decisions for target entity
disambiguation. However, most neural collective EL methods depend entirely upon
neural networks to automatically model the semantic dependencies between
different EL decisions, which lack of the guidance from external knowledge. In
this paper, we propose a novel end-to-end neural network with recurrent
random-walk layers for collective EL, which introduces external knowledge to
model the semantic interdependence between different EL decisions.
Specifically, we first establish a model based on local context features, and
then stack random-walk layers to reinforce the evidence for related EL
decisions into high-probability decisions, where the semantic interdependence
between candidate entities is mainly induced from an external knowledge base.
Finally, a semantic regularizer that preserves the collective EL decisions
consistency is incorporated into the conventional objective function, so that
the external knowledge base can be fully exploited in collective EL decisions.
Experimental results and in-depth analysis on various datasets show that our
model achieves better performance than other state-of-the-art models. Our code
and data are released at \url{https://github.com/DeepLearnXMU/RRWEL}.
