  Entity Alignment (EA) aims to match equivalent entities across different
Knowledge Graphs (KGs) and is an essential step of KG fusion. Current
mainstream methods -- neural EA models -- rely on training with seed alignment,
i.e., a set of pre-aligned entity pairs which are very costly to annotate. In
this paper, we devise a novel Active Learning (AL) framework for neural EA,
aiming to create highly informative seed alignment to obtain more effective EA
models with less annotation cost. Our framework tackles two main challenges
encountered when applying AL to EA: (1) How to exploit dependencies between
entities within the AL strategy. Most AL strategies assume that the data
instances to sample are independent and identically distributed. However,
entities in KGs are related. To address this challenge, we propose a
structure-aware uncertainty sampling strategy that can measure the uncertainty
of each entity as well as its impact on its neighbour entities in the KG. (2)
How to recognise entities that appear in one KG but not in the other KG (i.e.,
bachelors). Identifying bachelors would likely save annotation budget. To
address this challenge, we devise a bachelor recognizer paying attention to
alleviate the effect of sampling bias. Empirical results show that our proposed
AL strategy can significantly improve sampling quality with good generality
across different datasets, EA models and amount of bachelors.
