  Entity Alignment (EA) aims to find equivalent entities between two Knowledge
Graphs (KGs). While numerous neural EA models have been devised, they are
mainly learned using labelled data only. In this work, we argue that different
entities within one KG should have compatible counterparts in the other KG due
to the potential dependencies among the entities. Making compatible predictions
thus should be one of the goals of training an EA model along with fitting the
labelled data: this aspect however is neglected in current methods. To power
neural EA models with compatibility, we devise a training framework by
addressing three problems: (1) how to measure the compatibility of an EA model;
(2) how to inject the property of being compatible into an EA model; (3) how to
optimise parameters of the compatibility model. Extensive experiments on
widely-used datasets demonstrate the advantages of integrating compatibility
within EA models. In fact, state-of-the-art neural EA models trained within our
framework using just 5\% of the labelled data can achieve comparable
effectiveness with supervised training using 20\% of the labelled data.
